add yolo5
This commit is contained in:
parent
1166d90bd5
commit
e9049fb13c
12
README.md
12
README.md
@ -1,11 +1 @@
|
||||
# MOVE_AI
|
||||
|
||||
青岛理工大学QUT
|
||||
MOVE战队
|
||||
RoboMaster比赛
|
||||
所有上位机代码仓库。具体请查看分支
|
||||
包含步兵/英雄/无人机，
|
||||
哨兵，
|
||||
雷达，
|
||||
等兵种
|
||||
支持导航，自瞄，决策等功能。
|
||||
# MOVE 26单目相机雷达站
|
||||
222
third_party/yolov5/.dockerignore
vendored
Normal file
222
third_party/yolov5/.dockerignore
vendored
Normal file
@ -0,0 +1,222 @@
|
||||
# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------
|
||||
.git
|
||||
.cache
|
||||
.idea
|
||||
runs
|
||||
output
|
||||
coco
|
||||
storage.googleapis.com
|
||||
|
||||
data/samples/*
|
||||
**/results*.csv
|
||||
*.jpg
|
||||
|
||||
# Neural Network weights -----------------------------------------------------------------------------------------------
|
||||
**/*.pt
|
||||
**/*.pth
|
||||
**/*.onnx
|
||||
**/*.engine
|
||||
**/*.mlmodel
|
||||
**/*.torchscript
|
||||
**/*.torchscript.pt
|
||||
**/*.tflite
|
||||
**/*.h5
|
||||
**/*.pb
|
||||
*_saved_model/
|
||||
*_web_model/
|
||||
*_openvino_model/
|
||||
|
||||
# Below Copied From .gitignore -----------------------------------------------------------------------------------------
|
||||
# Below Copied From .gitignore -----------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
env/
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
wandb/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# celery beat schedule file
|
||||
celerybeat-schedule
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# dotenv
|
||||
.env
|
||||
|
||||
# virtualenv
|
||||
.venv*
|
||||
venv*/
|
||||
ENV*/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
|
||||
|
||||
# General
|
||||
.DS_Store
|
||||
.AppleDouble
|
||||
.LSOverride
|
||||
|
||||
# Icon must end with two \r
|
||||
Icon
|
||||
Icon?
|
||||
|
||||
# Thumbnails
|
||||
._*
|
||||
|
||||
# Files that might appear in the root of a volume
|
||||
.DocumentRevisions-V100
|
||||
.fseventsd
|
||||
.Spotlight-V100
|
||||
.TemporaryItems
|
||||
.Trashes
|
||||
.VolumeIcon.icns
|
||||
.com.apple.timemachine.donotpresent
|
||||
|
||||
# Directories potentially created on remote AFP share
|
||||
.AppleDB
|
||||
.AppleDesktop
|
||||
Network Trash Folder
|
||||
Temporary Items
|
||||
.apdisk
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
|
||||
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
|
||||
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
|
||||
|
||||
# User-specific stuff:
|
||||
.idea/*
|
||||
.idea/**/workspace.xml
|
||||
.idea/**/tasks.xml
|
||||
.idea/dictionaries
|
||||
.html # Bokeh Plots
|
||||
.pg # TensorFlow Frozen Graphs
|
||||
.avi # videos
|
||||
|
||||
# Sensitive or high-churn files:
|
||||
.idea/**/dataSources/
|
||||
.idea/**/dataSources.ids
|
||||
.idea/**/dataSources.local.xml
|
||||
.idea/**/sqlDataSources.xml
|
||||
.idea/**/dynamic.xml
|
||||
.idea/**/uiDesigner.xml
|
||||
|
||||
# Gradle:
|
||||
.idea/**/gradle.xml
|
||||
.idea/**/libraries
|
||||
|
||||
# CMake
|
||||
cmake-build-debug/
|
||||
cmake-build-release/
|
||||
|
||||
# Mongo Explorer plugin:
|
||||
.idea/**/mongoSettings.xml
|
||||
|
||||
## File-based project format:
|
||||
*.iws
|
||||
|
||||
## Plugin-specific files:
|
||||
|
||||
# IntelliJ
|
||||
out/
|
||||
|
||||
# mpeltonen/sbt-idea plugin
|
||||
.idea_modules/
|
||||
|
||||
# JIRA plugin
|
||||
atlassian-ide-plugin.xml
|
||||
|
||||
# Cursive Clojure plugin
|
||||
.idea/replstate.xml
|
||||
|
||||
# Crashlytics plugin (for Android Studio and IntelliJ)
|
||||
com_crashlytics_export_strings.xml
|
||||
crashlytics.properties
|
||||
crashlytics-build.properties
|
||||
fabric.properties
|
||||
2
third_party/yolov5/.gitattributes
vendored
Normal file
2
third_party/yolov5/.gitattributes
vendored
Normal file
@ -0,0 +1,2 @@
|
||||
# this drop notebooks from GitHub language stats
|
||||
*.ipynb linguist-vendored
|
||||
258
third_party/yolov5/.gitignore
vendored
Normal file
258
third_party/yolov5/.gitignore
vendored
Normal file
@ -0,0 +1,258 @@
|
||||
# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
|
||||
*.jpg
|
||||
*.jpeg
|
||||
*.png
|
||||
*.bmp
|
||||
*.tif
|
||||
*.tiff
|
||||
*.heic
|
||||
*.JPG
|
||||
*.JPEG
|
||||
*.PNG
|
||||
*.BMP
|
||||
*.TIF
|
||||
*.TIFF
|
||||
*.HEIC
|
||||
*.mp4
|
||||
*.mov
|
||||
*.MOV
|
||||
*.avi
|
||||
*.data
|
||||
*.json
|
||||
*.cfg
|
||||
!setup.cfg
|
||||
!cfg/yolov3*.cfg
|
||||
|
||||
storage.googleapis.com
|
||||
runs/*
|
||||
data/*
|
||||
data/images/*
|
||||
!data/*.yaml
|
||||
!data/hyps
|
||||
!data/scripts
|
||||
!data/images
|
||||
!data/images/zidane.jpg
|
||||
!data/images/bus.jpg
|
||||
!data/*.sh
|
||||
|
||||
results*.csv
|
||||
|
||||
# Datasets -------------------------------------------------------------------------------------------------------------
|
||||
coco/
|
||||
coco128/
|
||||
VOC/
|
||||
|
||||
# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
|
||||
*.m~
|
||||
*.mat
|
||||
!targets*.mat
|
||||
|
||||
# Neural Network weights -----------------------------------------------------------------------------------------------
|
||||
*.weights
|
||||
*.pt
|
||||
*.pb
|
||||
*.onnx
|
||||
*.engine
|
||||
*.mlmodel
|
||||
*.mlpackage
|
||||
*.torchscript
|
||||
*.tflite
|
||||
*.h5
|
||||
*_saved_model/
|
||||
*_web_model/
|
||||
*_openvino_model/
|
||||
*_paddle_model/
|
||||
darknet53.conv.74
|
||||
yolov3-tiny.conv.15
|
||||
|
||||
# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
env/
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
/wandb/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# celery beat schedule file
|
||||
celerybeat-schedule
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# dotenv
|
||||
.env
|
||||
|
||||
# virtualenv
|
||||
.venv*
|
||||
venv*/
|
||||
ENV*/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
|
||||
|
||||
# General
|
||||
.DS_Store
|
||||
.AppleDouble
|
||||
.LSOverride
|
||||
|
||||
# Icon must end with two \r
|
||||
Icon
|
||||
Icon?
|
||||
|
||||
# Thumbnails
|
||||
._*
|
||||
|
||||
# Files that might appear in the root of a volume
|
||||
.DocumentRevisions-V100
|
||||
.fseventsd
|
||||
.Spotlight-V100
|
||||
.TemporaryItems
|
||||
.Trashes
|
||||
.VolumeIcon.icns
|
||||
.com.apple.timemachine.donotpresent
|
||||
|
||||
# Directories potentially created on remote AFP share
|
||||
.AppleDB
|
||||
.AppleDesktop
|
||||
Network Trash Folder
|
||||
Temporary Items
|
||||
.apdisk
|
||||
|
||||
|
||||
# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
|
||||
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
|
||||
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
|
||||
|
||||
# User-specific stuff:
|
||||
.idea/*
|
||||
.idea/**/workspace.xml
|
||||
.idea/**/tasks.xml
|
||||
.idea/dictionaries
|
||||
.html # Bokeh Plots
|
||||
.pg # TensorFlow Frozen Graphs
|
||||
.avi # videos
|
||||
|
||||
# Sensitive or high-churn files:
|
||||
.idea/**/dataSources/
|
||||
.idea/**/dataSources.ids
|
||||
.idea/**/dataSources.local.xml
|
||||
.idea/**/sqlDataSources.xml
|
||||
.idea/**/dynamic.xml
|
||||
.idea/**/uiDesigner.xml
|
||||
|
||||
# Gradle:
|
||||
.idea/**/gradle.xml
|
||||
.idea/**/libraries
|
||||
|
||||
# CMake
|
||||
cmake-build-debug/
|
||||
cmake-build-release/
|
||||
|
||||
# Mongo Explorer plugin:
|
||||
.idea/**/mongoSettings.xml
|
||||
|
||||
## File-based project format:
|
||||
*.iws
|
||||
|
||||
## Plugin-specific files:
|
||||
|
||||
# IntelliJ
|
||||
out/
|
||||
|
||||
# mpeltonen/sbt-idea plugin
|
||||
.idea_modules/
|
||||
|
||||
# JIRA plugin
|
||||
atlassian-ide-plugin.xml
|
||||
|
||||
# Cursive Clojure plugin
|
||||
.idea/replstate.xml
|
||||
|
||||
# Crashlytics plugin (for Android Studio and IntelliJ)
|
||||
com_crashlytics_export_strings.xml
|
||||
crashlytics.properties
|
||||
crashlytics-build.properties
|
||||
fabric.properties
|
||||
14
third_party/yolov5/CITATION.cff
vendored
Normal file
14
third_party/yolov5/CITATION.cff
vendored
Normal file
@ -0,0 +1,14 @@
|
||||
cff-version: 1.2.0
|
||||
preferred-citation:
|
||||
type: software
|
||||
message: If you use YOLOv5, please cite it as below.
|
||||
authors:
|
||||
- family-names: Jocher
|
||||
given-names: Glenn
|
||||
orcid: "https://orcid.org/0000-0001-5950-6979"
|
||||
title: "YOLOv5 by Ultralytics"
|
||||
version: 7.0
|
||||
doi: 10.5281/zenodo.3908559
|
||||
date-released: 2020-5-29
|
||||
license: AGPL-3.0
|
||||
url: "https://github.com/ultralytics/yolov5"
|
||||
87
third_party/yolov5/CONTRIBUTING.md
vendored
Normal file
87
third_party/yolov5/CONTRIBUTING.md
vendored
Normal file
@ -0,0 +1,87 @@
|
||||
<a href="https://www.ultralytics.com/"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
|
||||
|
||||
# Contributing to YOLO 🚀
|
||||
|
||||
We value your input and are committed to making contributing to YOLO as easy and transparent as possible. Whether you're:
|
||||
|
||||
- Reporting a bug
|
||||
- Discussing the current state of the codebase
|
||||
- Submitting a fix
|
||||
- Proposing a new feature
|
||||
- Interested in becoming a maintainer
|
||||
|
||||
Ultralytics YOLO thrives thanks to the collective efforts of our community. Every improvement you contribute helps push the boundaries of what's possible in AI! 😃
|
||||
|
||||
## 🛠️ Submitting a Pull Request (PR)
|
||||
|
||||
Submitting a PR is straightforward! Here’s an example showing how to update `requirements.txt` in four simple steps:
|
||||
|
||||
### 1. Select the File to Update
|
||||
|
||||
Click on `requirements.txt` in the GitHub repository.
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
|
||||
|
||||
### 2. Click 'Edit this file'
|
||||
|
||||
Find the 'Edit this file' button in the top-right corner.
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
|
||||
|
||||
### 3. Make Your Changes
|
||||
|
||||
For example, update the `matplotlib` version from `3.2.2` to `3.3`.
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
|
||||
|
||||
### 4. Preview Changes and Submit Your PR
|
||||
|
||||
Click the **Preview changes** tab to review your updates. At the bottom, select 'Create a new branch for this commit', give your branch a descriptive name like `fix/matplotlib_version`, and click the green **Propose changes** button. Your PR is now submitted for review! 😃
|
||||
|
||||
<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
|
||||
|
||||
### PR Best Practices
|
||||
|
||||
To ensure your work is integrated smoothly, please:
|
||||
|
||||
- ✅ Make sure your PR is **up-to-date** with the `ultralytics/yolov5` `master` branch. If your branch is behind, update it using the 'Update branch' button or by running `git pull` and `git merge master` locally.
|
||||
|
||||
<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 15" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
|
||||
|
||||
- ✅ Ensure all YOLO Continuous Integration (CI) **checks are passing**.
|
||||
|
||||
<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 03" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
|
||||
|
||||
- ✅ Limit your changes to the **minimum** required for your bug fix or feature.
|
||||
_"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
|
||||
|
||||
## 🐛 Submitting a Bug Report
|
||||
|
||||
If you encounter an issue with YOLO, please submit a bug report!
|
||||
|
||||
To help us investigate, we need to be able to reproduce the problem. Follow these guidelines to provide what we need to get started:
|
||||
|
||||
When asking a question or reporting a bug, you'll get better help if you provide **code** that others can easily understand and use to **reproduce** the issue. This is known as a [minimum reproducible example](https://docs.ultralytics.com/help/minimum-reproducible-example/). Your code should be:
|
||||
|
||||
- ✅ **Minimal** – Use as little code as possible that still produces the issue
|
||||
- ✅ **Complete** – Include all parts needed for someone else to reproduce the problem
|
||||
- ✅ **Reproducible** – Test your code to ensure it actually reproduces the issue
|
||||
|
||||
Additionally, for [Ultralytics](https://www.ultralytics.com/) to assist you, your code should be:
|
||||
|
||||
- ✅ **Current** – Ensure your code is up-to-date with the latest [master branch](https://github.com/ultralytics/yolov5/tree/master). Use `git pull` or `git clone` to get the latest version and confirm your issue hasn't already been fixed.
|
||||
- ✅ **Unmodified** – The problem must be reproducible without any custom modifications to the repository. [Ultralytics](https://www.ultralytics.com/) does not provide support for custom code ⚠️.
|
||||
|
||||
If your issue meets these criteria, please close your current issue and open a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose), including your [minimum reproducible example](https://docs.ultralytics.com/help/minimum-reproducible-example/) to help us diagnose your problem.
|
||||
|
||||
## 📄 License
|
||||
|
||||
By contributing, you agree that your contributions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/).
|
||||
|
||||
---
|
||||
|
||||
For more details on contributing, check out the [Ultralytics open-source contributing guide](https://docs.ultralytics.com/help/contributing/), and explore our [Ultralytics blog](https://www.ultralytics.com/blog) for community highlights and best practices.
|
||||
|
||||
We welcome your contributions—thank you for helping make Ultralytics YOLO better! 🚀
|
||||
|
||||
[](https://github.com/ultralytics/ultralytics/graphs/contributors)
|
||||
661
third_party/yolov5/LICENSE
vendored
Normal file
661
third_party/yolov5/LICENSE
vendored
Normal file
@ -0,0 +1,661 @@
|
||||
GNU AFFERO GENERAL PUBLIC LICENSE
|
||||
Version 3, 19 November 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU Affero General Public License is a free, copyleft license for
|
||||
software and other kinds of works, specifically designed to ensure
|
||||
cooperation with the community in the case of network server software.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
our General Public Licenses are intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
them if you wish), that you receive source code or can get it if you
|
||||
want it, that you can change the software or use pieces of it in new
|
||||
free programs, and that you know you can do these things.
|
||||
|
||||
Developers that use our General Public Licenses protect your rights
|
||||
with two steps: (1) assert copyright on the software, and (2) offer
|
||||
you this License which gives you legal permission to copy, distribute
|
||||
and/or modify the software.
|
||||
|
||||
A secondary benefit of defending all users' freedom is that
|
||||
improvements made in alternate versions of the program, if they
|
||||
receive widespread use, become available for other developers to
|
||||
incorporate. Many developers of free software are heartened and
|
||||
encouraged by the resulting cooperation. However, in the case of
|
||||
software used on network servers, this result may fail to come about.
|
||||
The GNU General Public License permits making a modified version and
|
||||
letting the public access it on a server without ever releasing its
|
||||
source code to the public.
|
||||
|
||||
The GNU Affero General Public License is designed specifically to
|
||||
ensure that, in such cases, the modified source code becomes available
|
||||
to the community. It requires the operator of a network server to
|
||||
provide the source code of the modified version running there to the
|
||||
users of that server. Therefore, public use of a modified version, on
|
||||
a publicly accessible server, gives the public access to the source
|
||||
code of the modified version.
|
||||
|
||||
An older license, called the Affero General Public License and
|
||||
published by Affero, was designed to accomplish similar goals. This is
|
||||
a different license, not a version of the Affero GPL, but Affero has
|
||||
released a new version of the Affero GPL which permits relicensing under
|
||||
this license.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
TERMS AND CONDITIONS
|
||||
|
||||
0. Definitions.
|
||||
|
||||
"This License" refers to version 3 of the GNU Affero General Public License.
|
||||
|
||||
"Copyright" also means copyright-like laws that apply to other kinds of
|
||||
works, such as semiconductor masks.
|
||||
|
||||
"The Program" refers to any copyrightable work licensed under this
|
||||
License. Each licensee is addressed as "you". "Licensees" and
|
||||
"recipients" may be individuals or organizations.
|
||||
|
||||
To "modify" a work means to copy from or adapt all or part of the work
|
||||
in a fashion requiring copyright permission, other than the making of an
|
||||
exact copy. The resulting work is called a "modified version" of the
|
||||
earlier work or a work "based on" the earlier work.
|
||||
|
||||
A "covered work" means either the unmodified Program or a work based
|
||||
on the Program.
|
||||
|
||||
To "propagate" a work means to do anything with it that, without
|
||||
permission, would make you directly or secondarily liable for
|
||||
infringement under applicable copyright law, except executing it on a
|
||||
computer or modifying a private copy. Propagation includes copying,
|
||||
distribution (with or without modification), making available to the
|
||||
public, and in some countries other activities as well.
|
||||
|
||||
To "convey" a work means any kind of propagation that enables other
|
||||
parties to make or receive copies. Mere interaction with a user through
|
||||
a computer network, with no transfer of a copy, is not conveying.
|
||||
|
||||
An interactive user interface displays "Appropriate Legal Notices"
|
||||
to the extent that it includes a convenient and prominently visible
|
||||
feature that (1) displays an appropriate copyright notice, and (2)
|
||||
tells the user that there is no warranty for the work (except to the
|
||||
extent that warranties are provided), that licensees may convey the
|
||||
work under this License, and how to view a copy of this License. If
|
||||
the interface presents a list of user commands or options, such as a
|
||||
menu, a prominent item in the list meets this criterion.
|
||||
|
||||
1. Source Code.
|
||||
|
||||
The "source code" for a work means the preferred form of the work
|
||||
for making modifications to it. "Object code" means any non-source
|
||||
form of a work.
|
||||
|
||||
A "Standard Interface" means an interface that either is an official
|
||||
standard defined by a recognized standards body, or, in the case of
|
||||
interfaces specified for a particular programming language, one that
|
||||
is widely used among developers working in that language.
|
||||
|
||||
The "System Libraries" of an executable work include anything, other
|
||||
than the work as a whole, that (a) is included in the normal form of
|
||||
packaging a Major Component, but which is not part of that Major
|
||||
Component, and (b) serves only to enable use of the work with that
|
||||
Major Component, or to implement a Standard Interface for which an
|
||||
implementation is available to the public in source code form. A
|
||||
"Major Component", in this context, means a major essential component
|
||||
(kernel, window system, and so on) of the specific operating system
|
||||
(if any) on which the executable work runs, or a compiler used to
|
||||
produce the work, or an object code interpreter used to run it.
|
||||
|
||||
The "Corresponding Source" for a work in object code form means all
|
||||
the source code needed to generate, install, and (for an executable
|
||||
work) run the object code and to modify the work, including scripts to
|
||||
control those activities. However, it does not include the work's
|
||||
System Libraries, or general-purpose tools or generally available free
|
||||
programs which are used unmodified in performing those activities but
|
||||
which are not part of the work. For example, Corresponding Source
|
||||
includes interface definition files associated with source files for
|
||||
the work, and the source code for shared libraries and dynamically
|
||||
linked subprograms that the work is specifically designed to require,
|
||||
such as by intimate data communication or control flow between those
|
||||
subprograms and other parts of the work.
|
||||
|
||||
The Corresponding Source need not include anything that users
|
||||
can regenerate automatically from other parts of the Corresponding
|
||||
Source.
|
||||
|
||||
The Corresponding Source for a work in source code form is that
|
||||
same work.
|
||||
|
||||
2. Basic Permissions.
|
||||
|
||||
All rights granted under this License are granted for the term of
|
||||
copyright on the Program, and are irrevocable provided the stated
|
||||
conditions are met. This License explicitly affirms your unlimited
|
||||
permission to run the unmodified Program. The output from running a
|
||||
covered work is covered by this License only if the output, given its
|
||||
content, constitutes a covered work. This License acknowledges your
|
||||
rights of fair use or other equivalent, as provided by copyright law.
|
||||
|
||||
You may make, run and propagate covered works that you do not
|
||||
convey, without conditions so long as your license otherwise remains
|
||||
in force. You may convey covered works to others for the sole purpose
|
||||
of having them make modifications exclusively for you, or provide you
|
||||
with facilities for running those works, provided that you comply with
|
||||
the terms of this License in conveying all material for which you do
|
||||
not control copyright. Those thus making or running the covered works
|
||||
for you must do so exclusively on your behalf, under your direction
|
||||
and control, on terms that prohibit them from making any copies of
|
||||
your copyrighted material outside their relationship with you.
|
||||
|
||||
Conveying under any other circumstances is permitted solely under
|
||||
the conditions stated below. Sublicensing is not allowed; section 10
|
||||
makes it unnecessary.
|
||||
|
||||
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
|
||||
|
||||
No covered work shall be deemed part of an effective technological
|
||||
measure under any applicable law fulfilling obligations under article
|
||||
11 of the WIPO copyright treaty adopted on 20 December 1996, or
|
||||
similar laws prohibiting or restricting circumvention of such
|
||||
measures.
|
||||
|
||||
When you convey a covered work, you waive any legal power to forbid
|
||||
circumvention of technological measures to the extent such circumvention
|
||||
is effected by exercising rights under this License with respect to
|
||||
the covered work, and you disclaim any intention to limit operation or
|
||||
modification of the work as a means of enforcing, against the work's
|
||||
users, your or third parties' legal rights to forbid circumvention of
|
||||
technological measures.
|
||||
|
||||
4. Conveying Verbatim Copies.
|
||||
|
||||
You may convey verbatim copies of the Program's source code as you
|
||||
receive it, in any medium, provided that you conspicuously and
|
||||
appropriately publish on each copy an appropriate copyright notice;
|
||||
keep intact all notices stating that this License and any
|
||||
non-permissive terms added in accord with section 7 apply to the code;
|
||||
keep intact all notices of the absence of any warranty; and give all
|
||||
recipients a copy of this License along with the Program.
|
||||
|
||||
You may charge any price or no price for each copy that you convey,
|
||||
and you may offer support or warranty protection for a fee.
|
||||
|
||||
5. Conveying Modified Source Versions.
|
||||
|
||||
You may convey a work based on the Program, or the modifications to
|
||||
produce it from the Program, in the form of source code under the
|
||||
terms of section 4, provided that you also meet all of these conditions:
|
||||
|
||||
a) The work must carry prominent notices stating that you modified
|
||||
it, and giving a relevant date.
|
||||
|
||||
b) The work must carry prominent notices stating that it is
|
||||
released under this License and any conditions added under section
|
||||
7. This requirement modifies the requirement in section 4 to
|
||||
"keep intact all notices".
|
||||
|
||||
c) You must license the entire work, as a whole, under this
|
||||
License to anyone who comes into possession of a copy. This
|
||||
License will therefore apply, along with any applicable section 7
|
||||
additional terms, to the whole of the work, and all its parts,
|
||||
regardless of how they are packaged. This License gives no
|
||||
permission to license the work in any other way, but it does not
|
||||
invalidate such permission if you have separately received it.
|
||||
|
||||
d) If the work has interactive user interfaces, each must display
|
||||
Appropriate Legal Notices; however, if the Program has interactive
|
||||
interfaces that do not display Appropriate Legal Notices, your
|
||||
work need not make them do so.
|
||||
|
||||
A compilation of a covered work with other separate and independent
|
||||
works, which are not by their nature extensions of the covered work,
|
||||
and which are not combined with it such as to form a larger program,
|
||||
in or on a volume of a storage or distribution medium, is called an
|
||||
"aggregate" if the compilation and its resulting copyright are not
|
||||
used to limit the access or legal rights of the compilation's users
|
||||
beyond what the individual works permit. Inclusion of a covered work
|
||||
in an aggregate does not cause this License to apply to the other
|
||||
parts of the aggregate.
|
||||
|
||||
6. Conveying Non-Source Forms.
|
||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
of sections 4 and 5, provided that you also convey the
|
||||
machine-readable Corresponding Source under the terms of this License,
|
||||
in one of these ways:
|
||||
|
||||
a) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by the
|
||||
Corresponding Source fixed on a durable physical medium
|
||||
customarily used for software interchange.
|
||||
|
||||
b) Convey the object code in, or embodied in, a physical product
|
||||
(including a physical distribution medium), accompanied by a
|
||||
written offer, valid for at least three years and valid for as
|
||||
long as you offer spare parts or customer support for that product
|
||||
model, to give anyone who possesses the object code either (1) a
|
||||
copy of the Corresponding Source for all the software in the
|
||||
product that is covered by this License, on a durable physical
|
||||
medium customarily used for software interchange, for a price no
|
||||
more than your reasonable cost of physically performing this
|
||||
conveying of source, or (2) access to copy the
|
||||
Corresponding Source from a network server at no charge.
|
||||
|
||||
c) Convey individual copies of the object code with a copy of the
|
||||
written offer to provide the Corresponding Source. This
|
||||
alternative is allowed only occasionally and noncommercially, and
|
||||
only if you received the object code with such an offer, in accord
|
||||
with subsection 6b.
|
||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
place (gratis or for a charge), and offer equivalent access to the
|
||||
Corresponding Source in the same way through the same place at no
|
||||
further charge. You need not require recipients to copy the
|
||||
Corresponding Source along with the object code. If the place to
|
||||
copy the object code is a network server, the Corresponding Source
|
||||
may be on a different server (operated by you or a third party)
|
||||
that supports equivalent copying facilities, provided you maintain
|
||||
clear directions next to the object code saying where to find the
|
||||
Corresponding Source. Regardless of what server hosts the
|
||||
Corresponding Source, you remain obligated to ensure that it is
|
||||
available for as long as needed to satisfy these requirements.
|
||||
|
||||
e) Convey the object code using peer-to-peer transmission, provided
|
||||
you inform other peers where the object code and Corresponding
|
||||
Source of the work are being offered to the general public at no
|
||||
charge under subsection 6d.
|
||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
from the Corresponding Source as a System Library, need not be
|
||||
included in conveying the object code work.
|
||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
tangible personal property which is normally used for personal, family,
|
||||
or household purposes, or (2) anything designed or sold for incorporation
|
||||
into a dwelling. In determining whether a product is a consumer product,
|
||||
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||
product received by a particular user, "normally used" refers to a
|
||||
typical or common use of that class of product, regardless of the status
|
||||
of the particular user or of the way in which the particular user
|
||||
actually uses, or expects or is expected to use, the product. A product
|
||||
is a consumer product regardless of whether the product has substantial
|
||||
commercial, industrial or non-consumer uses, unless such uses represent
|
||||
the only significant mode of use of the product.
|
||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
procedures, authorization keys, or other information required to install
|
||||
and execute modified versions of a covered work in that User Product from
|
||||
a modified version of its Corresponding Source. The information must
|
||||
suffice to ensure that the continued functioning of the modified object
|
||||
code is in no case prevented or interfered with solely because
|
||||
modification has been made.
|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
specifically for use in, a User Product, and the conveying occurs as
|
||||
part of a transaction in which the right of possession and use of the
|
||||
User Product is transferred to the recipient in perpetuity or for a
|
||||
fixed term (regardless of how the transaction is characterized), the
|
||||
Corresponding Source conveyed under this section must be accompanied
|
||||
by the Installation Information. But this requirement does not apply
|
||||
if neither you nor any third party retains the ability to install
|
||||
modified object code on the User Product (for example, the work has
|
||||
been installed in ROM).
|
||||
|
||||
The requirement to provide Installation Information does not include a
|
||||
requirement to continue to provide support service, warranty, or updates
|
||||
for a work that has been modified or installed by the recipient, or for
|
||||
the User Product in which it has been modified or installed. Access to a
|
||||
network may be denied when the modification itself materially and
|
||||
adversely affects the operation of the network or violates the rules and
|
||||
protocols for communication across the network.
|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
in accord with this section must be in a format that is publicly
|
||||
documented (and with an implementation available to the public in
|
||||
source code form), and must require no special password or key for
|
||||
unpacking, reading or copying.
|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
License by making exceptions from one or more of its conditions.
|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
be treated as though they were included in this License, to the extent
|
||||
that they are valid under applicable law. If additional permissions
|
||||
apply only to part of the Program, that part may be used separately
|
||||
under those permissions, but the entire Program remains governed by
|
||||
this License without regard to the additional permissions.
|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
remove any additional permissions from that copy, or from any part of
|
||||
it. (Additional permissions may be written to require their own
|
||||
removal in certain cases when you modify the work.) You may place
|
||||
additional permissions on material, added by you to a covered work,
|
||||
for which you have or can give appropriate copyright permission.
|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
add to a covered work, you may (if authorized by the copyright holders of
|
||||
that material) supplement the terms of this License with terms:
|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
terms of sections 15 and 16 of this License; or
|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
author attributions in that material or in the Appropriate Legal
|
||||
Notices displayed by works containing it; or
|
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or
|
||||
requiring that modified versions of such material be marked in
|
||||
reasonable ways as different from the original version; or
|
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or
|
||||
authors of the material; or
|
||||
|
||||
e) Declining to grant rights under trademark law for use of some
|
||||
trade names, trademarks, or service marks; or
|
||||
|
||||
f) Requiring indemnification of licensors and authors of that
|
||||
material by anyone who conveys the material (or modified versions of
|
||||
it) with contractual assumptions of liability to the recipient, for
|
||||
any liability that these contractual assumptions directly impose on
|
||||
those licensors and authors.
|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
restrictions" within the meaning of section 10. If the Program as you
|
||||
received it, or any part of it, contains a notice stating that it is
|
||||
governed by this License along with a term that is a further
|
||||
restriction, you may remove that term. If a license document contains
|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Remote Network Interaction; Use with the GNU General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, if you modify the
|
||||
Program, your modified version must prominently offer all users
|
||||
interacting with it remotely through a computer network (if your version
|
||||
supports such interaction) an opportunity to receive the Corresponding
|
||||
Source of your version by providing access to the Corresponding Source
|
||||
from a network server at no charge, through some standard or customary
|
||||
means of facilitating copying of software. This Corresponding Source
|
||||
shall include the Corresponding Source for any work covered by version 3
|
||||
of the GNU General Public License that is incorporated pursuant to the
|
||||
following paragraph.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the work with which it is combined will remain governed by version
|
||||
3 of the GNU General Public License.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU Affero General Public License from time to time. Such new versions
|
||||
will be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU Affero General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU Affero General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU Affero General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU Affero General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU Affero General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU Affero General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If your software can interact with users remotely through a computer
|
||||
network, you should also make sure that it provides a way for users to
|
||||
get its source. For example, if your program is a web application, its
|
||||
interface could display a "Source" link that leads users to an archive
|
||||
of the code. There are many ways you could offer source, and different
|
||||
solutions will be better for different programs; see section 13 for the
|
||||
specific requirements.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU AGPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
512
third_party/yolov5/README.md
vendored
Normal file
512
third_party/yolov5/README.md
vendored
Normal file
@ -0,0 +1,512 @@
|
||||
<div align="center">
|
||||
<p>
|
||||
<a href="https://platform.ultralytics.com/ultralytics/yolo26" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
|
||||
</p>
|
||||
|
||||
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)
|
||||
|
||||
<div>
|
||||
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI Testing"></a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
|
||||
<a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
|
||||
<br>
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
||||
</div>
|
||||
<br>
|
||||
|
||||
Ultralytics YOLOv5 🚀 is a cutting-edge, state-of-the-art (SOTA) computer vision model developed by [Ultralytics](https://www.ultralytics.com/). Based on the [PyTorch](https://pytorch.org/) framework, YOLOv5 is renowned for its ease of use, speed, and accuracy. It incorporates insights and best practices from extensive research and development, making it a popular choice for a wide range of vision AI tasks, including [object detection](https://docs.ultralytics.com/tasks/detect/), [image segmentation](https://docs.ultralytics.com/tasks/segment/), and [image classification](https://docs.ultralytics.com/tasks/classify/).
|
||||
|
||||
We hope the resources here help you get the most out of YOLOv5. Please browse the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for detailed information, raise an issue on [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) for support, and join our [Discord community](https://discord.com/invite/ultralytics) for questions and discussions!
|
||||
|
||||
To request an Enterprise License, please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
|
||||
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<br>
|
||||
|
||||
## 🚀 YOLO11: The Next Evolution
|
||||
|
||||
We are excited to announce the launch of **Ultralytics YOLO11** 🚀, the latest advancement in our state-of-the-art (SOTA) vision models! Available now at the [Ultralytics YOLO GitHub repository](https://github.com/ultralytics/ultralytics), YOLO11 builds on our legacy of speed, precision, and ease of use. Whether you're tackling [object detection](https://docs.ultralytics.com/tasks/detect/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [pose estimation](https://docs.ultralytics.com/tasks/pose/), [image classification](https://docs.ultralytics.com/tasks/classify/), or [oriented object detection (OBB)](https://docs.ultralytics.com/tasks/obb/), YOLO11 delivers the performance and versatility needed to excel in diverse applications.
|
||||
|
||||
Get started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:
|
||||
|
||||
[](https://badge.fury.io/py/ultralytics) [](https://clickpy.clickhouse.com/dashboard/ultralytics)
|
||||
|
||||
```bash
|
||||
# Install the ultralytics package
|
||||
pip install ultralytics
|
||||
```
|
||||
|
||||
<div align="center">
|
||||
<a href="https://platform.ultralytics.com/ultralytics/yolo26" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="Ultralytics YOLO Performance Comparison"></a>
|
||||
</div>
|
||||
|
||||
## 📚 Documentation
|
||||
|
||||
See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing, and deployment. See below for quickstart examples.
|
||||
|
||||
<details open>
|
||||
<summary>Install</summary>
|
||||
|
||||
Clone the repository and install dependencies in a [**Python>=3.8.0**](https://www.python.org/) environment. Ensure you have [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) installed.
|
||||
|
||||
```bash
|
||||
# Clone the YOLOv5 repository
|
||||
git clone https://github.com/ultralytics/yolov5
|
||||
|
||||
# Navigate to the cloned directory
|
||||
cd yolov5
|
||||
|
||||
# Install required packages
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>Inference with PyTorch Hub</summary>
|
||||
|
||||
Use YOLOv5 via [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) for inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) are automatically downloaded from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Load a YOLOv5 model (options: yolov5n, yolov5s, yolov5m, yolov5l, yolov5x)
|
||||
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # Default: yolov5s
|
||||
|
||||
# Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list)
|
||||
img = "https://ultralytics.com/images/zidane.jpg" # Example image
|
||||
|
||||
# Perform inference (handles batching, resizing, normalization automatically)
|
||||
results = model(img)
|
||||
|
||||
# Process the results (options: .print(), .show(), .save(), .crop(), .pandas())
|
||||
results.print() # Print results to console
|
||||
results.show() # Display results in a window
|
||||
results.save() # Save results to runs/detect/exp
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Inference with detect.py</summary>
|
||||
|
||||
The `detect.py` script runs inference on various sources. It automatically downloads [models](https://github.com/ultralytics/yolov5/tree/master/models) from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saves the results to the `runs/detect` directory.
|
||||
|
||||
```bash
|
||||
# Run inference using a webcam
|
||||
python detect.py --weights yolov5s.pt --source 0
|
||||
|
||||
# Run inference on a local image file
|
||||
python detect.py --weights yolov5s.pt --source img.jpg
|
||||
|
||||
# Run inference on a local video file
|
||||
python detect.py --weights yolov5s.pt --source vid.mp4
|
||||
|
||||
# Run inference on a screen capture
|
||||
python detect.py --weights yolov5s.pt --source screen
|
||||
|
||||
# Run inference on a directory of images
|
||||
python detect.py --weights yolov5s.pt --source path/to/images/
|
||||
|
||||
# Run inference on a text file listing image paths
|
||||
python detect.py --weights yolov5s.pt --source list.txt
|
||||
|
||||
# Run inference on a text file listing stream URLs
|
||||
python detect.py --weights yolov5s.pt --source list.streams
|
||||
|
||||
# Run inference using a glob pattern for images
|
||||
python detect.py --weights yolov5s.pt --source 'path/to/*.jpg'
|
||||
|
||||
# Run inference on a YouTube video URL
|
||||
python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4'
|
||||
|
||||
# Run inference on an RTSP, RTMP, or HTTP stream
|
||||
python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Training</summary>
|
||||
|
||||
The commands below demonstrate how to reproduce YOLOv5 [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/) results. Both [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) are downloaded automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are approximately 1/2/4/6/8 days on a single [NVIDIA V100 GPU](https://www.nvidia.com/en-us/data-center/v100/). Using [Multi-GPU training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) can significantly reduce training time. Use the largest `--batch-size` your hardware allows, or use `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). The batch sizes shown below are for V100-16GB GPUs.
|
||||
|
||||
```bash
|
||||
# Train YOLOv5n on COCO for 300 epochs
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
|
||||
|
||||
# Train YOLOv5s on COCO for 300 epochs
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64
|
||||
|
||||
# Train YOLOv5m on COCO for 300 epochs
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40
|
||||
|
||||
# Train YOLOv5l on COCO for 300 epochs
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24
|
||||
|
||||
# Train YOLOv5x on COCO for 300 epochs
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16
|
||||
```
|
||||
|
||||
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" alt="YOLOv5 Training Results">
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>Tutorials</summary>
|
||||
|
||||
- **[Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **RECOMMENDED**: Learn how to train YOLOv5 on your own datasets.
|
||||
- **[Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️: Improve your model's performance with expert tips.
|
||||
- **[Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**: Speed up training using multiple GPUs.
|
||||
- **[PyTorch Hub Integration](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **NEW**: Easily load models using PyTorch Hub.
|
||||
- **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀: Convert your models to various deployment formats like [ONNX](https://onnx.ai/) or [TensorRT](https://developer.nvidia.com/tensorrt).
|
||||
- **[NVIDIA Jetson Deployment](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **NEW**: Deploy YOLOv5 on [NVIDIA Jetson](https://developer.nvidia.com/embedded-computing) devices.
|
||||
- **[Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**: Enhance prediction accuracy with TTA.
|
||||
- **[Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**: Combine multiple models for better performance.
|
||||
- **[Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**: Optimize models for size and speed.
|
||||
- **[Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**: Automatically find the best training hyperparameters.
|
||||
- **[Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**: Adapt pretrained models to new tasks efficiently using [transfer learning](https://www.ultralytics.com/glossary/transfer-learning).
|
||||
- **[Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **NEW**: Understand the YOLOv5 model architecture.
|
||||
- **[Ultralytics Platform Training](https://platform.ultralytics.com)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics Platform.
|
||||
- **[ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**: Integrate with [ClearML](https://clear.ml/) for experiment tracking.
|
||||
- **[Neural Magic DeepSparse Integration](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**: Accelerate inference with DeepSparse.
|
||||
- **[Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **NEW**: Log experiments using [Comet ML](https://www.comet.com/site/).
|
||||
|
||||
</details>
|
||||
|
||||
## 🧩 Integrations
|
||||
|
||||
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/).
|
||||
|
||||
<a href="https://docs.ultralytics.com/integrations/" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
|
||||
</a>
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://platform.ultralytics.com">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics Platform logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.ultralytics.com/integrations/weights-biases/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="Weights & Biases logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.ultralytics.com/integrations/comet/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.ultralytics.com/integrations/neural-magic/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
|
||||
</div>
|
||||
|
||||
| Ultralytics Platform 🌟 | Weights & Biases | Comet | Neural Magic |
|
||||
| :--------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
|
||||
| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics Platform](https://platform.ultralytics.com). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |
|
||||
|
||||
## ⭐ Ultralytics Platform
|
||||
|
||||
Experience seamless AI development with [Ultralytics Platform](https://platform.ultralytics.com) ⭐, the ultimate platform for building, training, and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models. Visualize datasets, train [YOLOv5](https://docs.ultralytics.com/models/yolov5/) and [YOLOv8](https://docs.ultralytics.com/models/yolov8/) 🚀 models, and deploy them to real-world applications without writing any code. Transform images into actionable insights using our cutting-edge tools and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** today!
|
||||
|
||||
<a align="center" href="https://platform.ultralytics.com" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics Platform Platform Screenshot"></a>
|
||||
|
||||
## 🤔 Why YOLOv5?
|
||||
|
||||
YOLOv5 is designed for simplicity and ease of use. We prioritize real-world performance and accessibility.
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png" alt="YOLOv5 Performance Chart"></p>
|
||||
<details>
|
||||
<summary>YOLOv5-P5 640 Figure</summary>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png" alt="YOLOv5 P5 640 Performance Chart"></p>
|
||||
</details>
|
||||
<details>
|
||||
<summary>Figure Notes</summary>
|
||||
|
||||
- **COCO AP val** denotes the [mean Average Precision (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map) at [Intersection over Union (IoU)](https://www.ultralytics.com/glossary/intersection-over-union-iou) thresholds from 0.5 to 0.95, measured on the 5,000-image [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco/) across various inference sizes (256 to 1536 pixels).
|
||||
- **GPU Speed** measures the average inference time per image on the [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco/) using an [AWS p3.2xlarge V100 instance](https://aws.amazon.com/ec2/instance-types/p4/) with a batch size of 32.
|
||||
- **EfficientDet** data is sourced from the [google/automl repository](https://github.com/google/automl) at batch size 8.
|
||||
- **Reproduce** these results using the command: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
||||
|
||||
</details>
|
||||
|
||||
### Pretrained Checkpoints
|
||||
|
||||
This table shows the performance metrics for various YOLOv5 models trained on the COCO dataset.
|
||||
|
||||
| Model | Size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
|
||||
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
|
||||
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
|
||||
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
|
||||
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
|
||||
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
|
||||
| | | | | | | | | |
|
||||
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
|
||||
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
|
||||
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
|
||||
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
|
||||
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
|
||||
|
||||
<details>
|
||||
<summary>Table Notes</summary>
|
||||
|
||||
- All checkpoints were trained for 300 epochs using default settings. Nano (n) and Small (s) models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyperparameters, while Medium (m), Large (l), and Extra-Large (x) models use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
|
||||
- **mAP<sup>val</sup>** values represent single-model, single-scale performance on the [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco/).<br>Reproduce using: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
- **Speed** metrics are averaged over COCO val images using an [AWS p3.2xlarge V100 instance](https://aws.amazon.com/ec2/instance-types/p4/). Non-Maximum Suppression (NMS) time (~1 ms/image) is not included.<br>Reproduce using: `python val.py --data coco.yaml --img 640 --task speed --batch 1`
|
||||
- **TTA** ([Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)) includes reflection and scale augmentations for improved accuracy.<br>Reproduce using: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
||||
|
||||
</details>
|
||||
|
||||
## 🖼️ Segmentation
|
||||
|
||||
The YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) introduced [instance segmentation](https://docs.ultralytics.com/tasks/segment/) models that achieve state-of-the-art performance. These models are designed for easy training, validation, and deployment. For full details, see the [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and explore the [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart examples.
|
||||
|
||||
<details>
|
||||
<summary>Segmentation Checkpoints</summary>
|
||||
|
||||
<div align="center">
|
||||
<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
|
||||
<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png" alt="YOLOv5 Segmentation Performance Chart"></a>
|
||||
</div>
|
||||
|
||||
YOLOv5 segmentation models were trained on the [COCO dataset](https://docs.ultralytics.com/datasets/segment/coco/) for 300 epochs at an image size of 640 pixels using A100 GPUs. Models were exported to [ONNX](https://onnx.ai/) FP32 for CPU speed tests and [TensorRT](https://developer.nvidia.com/tensorrt) FP16 for GPU speed tests. All speed tests were conducted on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for reproducibility.
|
||||
|
||||
| Model | Size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train Time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
|
||||
| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
|
||||
| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
|
||||
| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
|
||||
| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
|
||||
| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
|
||||
|
||||
- All checkpoints were trained for 300 epochs using the SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at an image size of 640 pixels, using default settings.<br>Training runs are logged at [https://wandb.ai/glenn-jocher/YOLOv5_v70_official](https://wandb.ai/glenn-jocher/YOLOv5_v70_official).
|
||||
- **Accuracy** values represent single-model, single-scale performance on the COCO dataset.<br>Reproduce using: `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
|
||||
- **Speed** metrics are averaged over 100 inference images using a [Colab Pro A100 High-RAM instance](https://colab.research.google.com/signup). Values indicate inference speed only (NMS adds approximately 1ms per image).<br>Reproduce using: `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
|
||||
- **Export** to ONNX (FP32) and TensorRT (FP16) was performed using `export.py`.<br>Reproduce using: `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Segmentation Usage Examples <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### Train
|
||||
|
||||
YOLOv5 segmentation training supports automatic download of the [COCO128-seg dataset](https://docs.ultralytics.com/datasets/segment/coco8-seg/) via the `--data coco128-seg.yaml` argument. For the full [COCO-segments dataset](https://docs.ultralytics.com/datasets/segment/coco/), download it manually using `bash data/scripts/get_coco.sh --train --val --segments` and then train with `python train.py --data coco.yaml`.
|
||||
|
||||
```bash
|
||||
# Train on a single GPU
|
||||
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
|
||||
|
||||
# Train using Multi-GPU Distributed Data Parallel (DDP)
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### Val
|
||||
|
||||
Validate the mask [mean Average Precision (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map) of YOLOv5s-seg on the COCO dataset:
|
||||
|
||||
```bash
|
||||
# Download COCO validation segments split (780MB, 5000 images)
|
||||
bash data/scripts/get_coco.sh --val --segments
|
||||
|
||||
# Validate the model
|
||||
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640
|
||||
```
|
||||
|
||||
### Predict
|
||||
|
||||
Use the pretrained YOLOv5m-seg.pt model to perform segmentation on `bus.jpg`:
|
||||
|
||||
```bash
|
||||
# Run prediction
|
||||
python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
# Load model from PyTorch Hub (Note: Inference support might vary)
|
||||
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5m-seg.pt")
|
||||
```
|
||||
|
||||
|  |  |
|
||||
| :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |
|
||||
|
||||
### Export
|
||||
|
||||
Export the YOLOv5s-seg model to ONNX and TensorRT formats:
|
||||
|
||||
```bash
|
||||
# Export model
|
||||
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## 🏷️ Classification
|
||||
|
||||
YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases/v6.2) introduced support for [image classification](https://docs.ultralytics.com/tasks/classify/) model training, validation, and deployment. Check the [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) for details and the [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart guides.
|
||||
|
||||
<details>
|
||||
<summary>Classification Checkpoints</summary>
|
||||
|
||||
<br>
|
||||
|
||||
YOLOv5-cls classification models were trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) for 90 epochs using a 4xA100 instance. [ResNet](https://arxiv.org/abs/1512.03385) and [EfficientNet](https://arxiv.org/abs/1905.11946) models were trained alongside under identical settings for comparison. Models were exported to [ONNX](https://onnx.ai/) FP32 (CPU speed tests) and [TensorRT](https://developer.nvidia.com/tensorrt) FP16 (GPU speed tests). All speed tests were run on Google [Colab Pro](https://colab.research.google.com/signup) for reproducibility.
|
||||
|
||||
| Model | Size<br><sup>(pixels) | Acc<br><sup>top1 | Acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
|
||||
| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
|
||||
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
|
||||
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
|
||||
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
|
||||
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
|
||||
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
|
||||
| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
|
||||
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
|
||||
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
|
||||
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
|
||||
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
|
||||
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
|
||||
|
||||
<details>
|
||||
<summary>Table Notes (click to expand)</summary>
|
||||
|
||||
- All checkpoints were trained for 90 epochs using the SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at an image size of 224 pixels, using default settings.<br>Training runs are logged at [https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2](https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2).
|
||||
- **Accuracy** values (top-1 and top-5) represent single-model, single-scale performance on the [ImageNet-1k dataset](https://docs.ultralytics.com/datasets/classify/imagenet/).<br>Reproduce using: `python classify/val.py --data ../datasets/imagenet --img 224`
|
||||
- **Speed** metrics are averaged over 100 inference images using a Google [Colab Pro V100 High-RAM instance](https://colab.research.google.com/signup).<br>Reproduce using: `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
|
||||
- **Export** to ONNX (FP32) and TensorRT (FP16) was performed using `export.py`.<br>Reproduce using: `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
|
||||
|
||||
</details>
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Classification Usage Examples <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### Train
|
||||
|
||||
YOLOv5 classification training supports automatic download for datasets like [MNIST](https://docs.ultralytics.com/datasets/classify/mnist/), [Fashion-MNIST](https://docs.ultralytics.com/datasets/classify/fashion-mnist/), [CIFAR10](https://docs.ultralytics.com/datasets/classify/cifar10/), [CIFAR100](https://docs.ultralytics.com/datasets/classify/cifar100/), [Imagenette](https://docs.ultralytics.com/datasets/classify/imagenette/), [Imagewoof](https://docs.ultralytics.com/datasets/classify/imagewoof/), and [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) using the `--data` argument. For example, start training on MNIST with `--data mnist`.
|
||||
|
||||
```bash
|
||||
# Train on a single GPU using CIFAR-100 dataset
|
||||
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
|
||||
|
||||
# Train using Multi-GPU DDP on ImageNet dataset
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### Val
|
||||
|
||||
Validate the accuracy of the YOLOv5m-cls model on the ImageNet-1k validation dataset:
|
||||
|
||||
```bash
|
||||
# Download ImageNet validation split (6.3GB, 50,000 images)
|
||||
bash data/scripts/get_imagenet.sh --val
|
||||
|
||||
# Validate the model
|
||||
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224
|
||||
```
|
||||
|
||||
### Predict
|
||||
|
||||
Use the pretrained YOLOv5s-cls.pt model to classify the image `bus.jpg`:
|
||||
|
||||
```bash
|
||||
# Run prediction
|
||||
python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
# Load model from PyTorch Hub
|
||||
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")
|
||||
```
|
||||
|
||||
### Export
|
||||
|
||||
Export trained YOLOv5s-cls, ResNet50, and EfficientNet_b0 models to ONNX and TensorRT formats:
|
||||
|
||||
```bash
|
||||
# Export models
|
||||
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## ☁️ Environments
|
||||
|
||||
Get started quickly with our pre-configured environments. Click the icons below for setup details.
|
||||
|
||||
<div align="center">
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook" title="Run on Paperspace Gradient">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" title="Open in Google Colab">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5" title="Open in Kaggle">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5" title="Pull Docker Image">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/" title="AWS Quickstart Guide">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/" title="GCP Quickstart Guide">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
|
||||
</div>
|
||||
|
||||
## 🤝 Contribute
|
||||
|
||||
We welcome your contributions! Making YOLOv5 accessible and effective is a community effort. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Share your feedback through the [YOLOv5 Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). Thank you to all our contributors for making YOLOv5 better!
|
||||
|
||||
[](https://github.com/ultralytics/yolov5/graphs/contributors)
|
||||
|
||||
## 📜 License
|
||||
|
||||
Ultralytics provides two licensing options to meet different needs:
|
||||
|
||||
- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-v3) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.
|
||||
- **Enterprise License**: Tailored for commercial applications, this license allows seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
|
||||
|
||||
## 📧 Contact
|
||||
|
||||
For bug reports and feature requests related to YOLOv5, please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For general questions, discussions, and community support, join our [Discord server](https://discord.com/invite/ultralytics)!
|
||||
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
512
third_party/yolov5/README.zh-CN.md
vendored
Normal file
512
third_party/yolov5/README.zh-CN.md
vendored
Normal file
@ -0,0 +1,512 @@
|
||||
<div align="center">
|
||||
<p>
|
||||
<a href="https://platform.ultralytics.com/ultralytics/yolo26" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO 横幅"></a>
|
||||
</p>
|
||||
|
||||
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)
|
||||
|
||||
<div>
|
||||
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI 测试"></a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker 拉取次数"></a>
|
||||
<a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics 论坛" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
|
||||
<br>
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="在 Gradient 上运行"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="在 Kaggle 中打开"></a>
|
||||
</div>
|
||||
<br>
|
||||
|
||||
Ultralytics YOLOv5 🚀 是由 [Ultralytics](https://www.ultralytics.com/) 开发的尖端、达到业界顶尖水平（SOTA）的计算机视觉模型。基于 [PyTorch](https://pytorch.org/) 框架，YOLOv5 以其易用性、速度和准确性而闻名。它融合了广泛研究和开发的见解与最佳实践，使其成为各种视觉 AI 任务的热门选择，包括[目标检测](https://docs.ultralytics.com/tasks/detect/)、[图像分割](https://docs.ultralytics.com/tasks/segment/)和[图像分类](https://docs.ultralytics.com/tasks/classify/)。
|
||||
|
||||
我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 [YOLOv5 文档](https://docs.ultralytics.com/yolov5/)获取详细信息，在 [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) 上提出 issue 以获得支持，并加入我们的 [Discord 社区](https://discord.com/invite/ultralytics)进行提问和讨论！
|
||||
|
||||
如需申请企业许可证，请填写 [Ultralytics 授权许可](https://www.ultralytics.com/license) 表格。
|
||||
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<br>
|
||||
|
||||
## 🚀 YOLO11：下一代进化
|
||||
|
||||
我们激动地宣布推出 **Ultralytics YOLO11** 🚀，这是我们业界顶尖（SOTA）视觉模型的最新进展！YOLO11 现已在 [Ultralytics YOLO GitHub 仓库](https://github.com/ultralytics/ultralytics)发布，它继承了我们速度快、精度高和易于使用的传统。无论您是处理[目标检测](https://docs.ultralytics.com/tasks/detect/)、[实例分割](https://docs.ultralytics.com/tasks/segment/)、[姿态估计](https://docs.ultralytics.com/tasks/pose/)、[图像分类](https://docs.ultralytics.com/tasks/classify/)还是[旋转目标检测 (OBB)](https://docs.ultralytics.com/tasks/obb/)，YOLO11 都能提供在多样化应用中脱颖而出所需的性能和多功能性。
|
||||
|
||||
立即开始，释放 YOLO11 的全部潜力！访问 [Ultralytics 文档](https://docs.ultralytics.com/)获取全面的指南和资源：
|
||||
|
||||
[](https://badge.fury.io/py/ultralytics) [](https://clickpy.clickhouse.com/dashboard/ultralytics)
|
||||
|
||||
```bash
|
||||
# 安装 ultralytics 包
|
||||
pip install ultralytics
|
||||
```
|
||||
|
||||
<div align="center">
|
||||
<a href="https://platform.ultralytics.com/ultralytics/yolo26" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="Ultralytics YOLO 性能比较"></a>
|
||||
</div>
|
||||
|
||||
## 📚 文档
|
||||
|
||||
请参阅 [YOLOv5 文档](https://docs.ultralytics.com/yolov5/)，了解有关训练、测试和部署的完整文档。请参阅下方的快速入门示例。
|
||||
|
||||
<details open>
|
||||
<summary>安装</summary>
|
||||
|
||||
克隆仓库并在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装依赖项。确保您已安装 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。
|
||||
|
||||
```bash
|
||||
# 克隆 YOLOv5 仓库
|
||||
git clone https://github.com/ultralytics/yolov5
|
||||
|
||||
# 导航到克隆的目录
|
||||
cd yolov5
|
||||
|
||||
# 安装所需的包
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>使用 PyTorch Hub 进行推理</summary>
|
||||
|
||||
通过 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 使用 YOLOv5 进行推理。[模型](https://github.com/ultralytics/yolov5/tree/master/models) 会自动从最新的 YOLOv5 [发布版本](https://github.com/ultralytics/yolov5/releases)下载。
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
# 加载 YOLOv5 模型（选项：yolov5n, yolov5s, yolov5m, yolov5l, yolov5x）
|
||||
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # 默认：yolov5s
|
||||
|
||||
# 定义输入图像源（URL、本地文件、PIL 图像、OpenCV 帧、numpy 数组或列表）
|
||||
img = "https://ultralytics.com/images/zidane.jpg" # 示例图像
|
||||
|
||||
# 执行推理（自动处理批处理、调整大小、归一化）
|
||||
results = model(img)
|
||||
|
||||
# 处理结果（选项：.print(), .show(), .save(), .crop(), .pandas()）
|
||||
results.print() # 将结果打印到控制台
|
||||
results.show() # 在窗口中显示结果
|
||||
results.save() # 将结果保存到 runs/detect/exp
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>使用 detect.py 进行推理</summary>
|
||||
|
||||
`detect.py` 脚本在各种来源上运行推理。它会自动从最新的 YOLOv5 [发布版本](https://github.com/ultralytics/yolov5/releases)下载[模型](https://github.com/ultralytics/yolov5/tree/master/models)，并将结果保存到 `runs/detect` 目录。
|
||||
|
||||
```bash
|
||||
# 使用网络摄像头运行推理
|
||||
python detect.py --weights yolov5s.pt --source 0
|
||||
|
||||
# 对本地图像文件运行推理
|
||||
python detect.py --weights yolov5s.pt --source img.jpg
|
||||
|
||||
# 对本地视频文件运行推理
|
||||
python detect.py --weights yolov5s.pt --source vid.mp4
|
||||
|
||||
# 对屏幕截图运行推理
|
||||
python detect.py --weights yolov5s.pt --source screen
|
||||
|
||||
# 对图像目录运行推理
|
||||
python detect.py --weights yolov5s.pt --source path/to/images/
|
||||
|
||||
# 对列出图像路径的文本文件运行推理
|
||||
python detect.py --weights yolov5s.pt --source list.txt
|
||||
|
||||
# 对列出流 URL 的文本文件运行推理
|
||||
python detect.py --weights yolov5s.pt --source list.streams
|
||||
|
||||
# 使用 glob 模式对图像运行推理
|
||||
python detect.py --weights yolov5s.pt --source 'path/to/*.jpg'
|
||||
|
||||
# 对 YouTube 视频 URL 运行推理
|
||||
python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4'
|
||||
|
||||
# 对 RTSP、RTMP 或 HTTP 流运行推理
|
||||
python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>训练</summary>
|
||||
|
||||
以下命令演示了如何复现 YOLOv5 在 [COCO 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上的结果。[模型](https://github.com/ultralytics/yolov5/tree/master/models)和[数据集](https://github.com/ultralytics/yolov5/tree/master/data)都会自动从最新的 YOLOv5 [发布版本](https://github.com/ultralytics/yolov5/releases)下载。YOLOv5n/s/m/l/x 的训练时间在单个 [NVIDIA V100 GPU](https://www.nvidia.com/en-us/data-center/v100/) 上大约需要 1/2/4/6/8 天。使用[多 GPU 训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)可以显著减少训练时间。请使用硬件允许的最大 `--batch-size`，或使用 `--batch-size -1` 以启用 YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092)。下面显示的批处理大小适用于 V100-16GB GPU。
|
||||
|
||||
```bash
|
||||
# 在 COCO 上训练 YOLOv5n 300 个周期
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
|
||||
|
||||
# 在 COCO 上训练 YOLOv5s 300 个周期
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64
|
||||
|
||||
# 在 COCO 上训练 YOLOv5m 300 个周期
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40
|
||||
|
||||
# 在 COCO 上训练 YOLOv5l 300 个周期
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24
|
||||
|
||||
# 在 COCO 上训练 YOLOv5x 300 个周期
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16
|
||||
```
|
||||
|
||||
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" alt="YOLOv5 训练结果">
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>教程</summary>
|
||||
|
||||
- **[训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **推荐**：学习如何在您自己的数据集上训练 YOLOv5。
|
||||
- **[获得最佳训练结果的技巧](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️：利用专家技巧提升模型性能。
|
||||
- **[多 GPU 训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**：使用多个 GPU 加速训练。
|
||||
- **[PyTorch Hub 集成](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **新增**：使用 PyTorch Hub 轻松加载模型。
|
||||
- **[模型导出 (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀：将您的模型转换为各种部署格式，如 [ONNX](https://onnx.ai/) 或 [TensorRT](https://developer.nvidia.com/tensorrt)。
|
||||
- **[NVIDIA Jetson 部署](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **新增**：在 [NVIDIA Jetson](https://developer.nvidia.com/embedded-computing) 设备上部署 YOLOv5。
|
||||
- **[测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**：使用 TTA 提高预测准确性。
|
||||
- **[模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**：组合多个模型以获得更好的性能。
|
||||
- **[模型剪枝/稀疏化](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**：优化模型的大小和速度。
|
||||
- **[超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**：自动找到最佳训练超参数。
|
||||
- **[使用冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**：使用[迁移学习](https://www.ultralytics.com/glossary/transfer-learning)高效地将预训练模型应用于新任务。
|
||||
- **[架构摘要](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **新增**：了解 YOLOv5 模型架构。
|
||||
- **[Ultralytics Platform 训练](https://platform.ultralytics.com)** 🚀 **推荐**：使用 Ultralytics Platform 训练和部署 YOLO 模型。
|
||||
- **[ClearML 日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**：与 [ClearML](https://clear.ml/) 集成以进行实验跟踪。
|
||||
- **[Neural Magic DeepSparse 集成](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**：使用 DeepSparse 加速推理。
|
||||
- **[Comet 日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **新增**：使用 [Comet ML](https://www.comet.com/site/) 记录实验。
|
||||
|
||||
</details>
|
||||
|
||||
## 🧩 集成
|
||||
|
||||
我们与领先 AI 平台的关键集成扩展了 Ultralytics 产品的功能，增强了诸如数据集标注、训练、可视化和模型管理等任务。了解 Ultralytics 如何与 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/)、[Comet ML](https://docs.ultralytics.com/integrations/comet/)、[Roboflow](https://docs.ultralytics.com/integrations/roboflow/) 和 [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 等合作伙伴协作，优化您的 AI 工作流程。在 [Ultralytics 集成](https://docs.ultralytics.com/integrations/) 探索更多信息。
|
||||
|
||||
<a href="https://docs.ultralytics.com/integrations/" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics 主动学习集成">
|
||||
</a>
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://platform.ultralytics.com">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics Platform logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.ultralytics.com/integrations/weights-biases/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="Weights & Biases logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.ultralytics.com/integrations/comet/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
|
||||
<a href="https://docs.ultralytics.com/integrations/neural-magic/">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
|
||||
</div>
|
||||
|
||||
| Ultralytics Platform 🌟 | Weights & Biases | Comet | Neural Magic |
|
||||
| :----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------: |
|
||||
| 简化 YOLO 工作流程：使用 [Ultralytics Platform](https://platform.ultralytics.com) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/) 跟踪实验、超参数和结果。 | 永久免费的 [Comet ML](https://docs.ultralytics.com/integrations/comet/) 让您保存 YOLO 模型、恢复训练并交互式地可视化预测。 | 使用 [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/) 将 YOLO 推理速度提高多达 6 倍。 |
|
||||
|
||||
## ⭐ Ultralytics Platform
|
||||
|
||||
通过 [Ultralytics Platform](https://platform.ultralytics.com) ⭐ 体验无缝的 AI 开发，这是构建、训练和部署[计算机视觉](https://www.ultralytics.com/glossary/computer-vision-cv)模型的终极平台。可视化数据集，训练 [YOLOv5](https://docs.ultralytics.com/models/yolov5/) 和 [YOLOv8](https://docs.ultralytics.com/models/yolov8/) 🚀 模型，并将它们部署到实际应用中，无需编写任何代码。使用我们尖端的工具和用户友好的 [Ultralytics App](https://www.ultralytics.com/app-install) 将图像转化为可操作的见解。今天就**免费**开始您的旅程吧！
|
||||
|
||||
<a align="center" href="https://platform.ultralytics.com" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics Platform 平台截图"></a>
|
||||
|
||||
## 🤔 为何选择 YOLOv5？
|
||||
|
||||
YOLOv5 的设计旨在简单易用。我们优先考虑实际性能和可访问性。
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png" alt="YOLOv5 性能图表"></p>
|
||||
<details>
|
||||
<summary>YOLOv5-P5 640 图表</summary>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png" alt="YOLOv5 P5 640 性能图表"></p>
|
||||
</details>
|
||||
<details>
|
||||
<summary>图表说明</summary>
|
||||
|
||||
- **COCO AP val** 表示在 [交并比 (IoU)](https://www.ultralytics.com/glossary/intersection-over-union-iou) 阈值从 0.5 到 0.95 范围内的[平均精度均值 (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map)，在包含 5000 张图像的 [COCO val2017 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上，使用各种推理尺寸（256 到 1536 像素）测量得出。
|
||||
- **GPU Speed** 使用批处理大小为 32 的 [AWS p3.2xlarge V100 实例](https://aws.amazon.com/ec2/instance-types/p4/)，测量在 [COCO val2017 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上每张图像的平均推理时间。
|
||||
- **EfficientDet** 数据来源于 [google/automl 仓库](https://github.com/google/automl)，批处理大小为 8。
|
||||
- **复现**这些结果请使用命令：`python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
||||
|
||||
</details>
|
||||
|
||||
### 预训练权重
|
||||
|
||||
此表显示了在 COCO 数据集上训练的各种 YOLOv5 模型的性能指标。
|
||||
|
||||
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 速度<br><sup>CPU b1<br>(毫秒) | 速度<br><sup>V100 b1<br>(毫秒) | 速度<br><sup>V100 b32<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------- | ----------------------------- | ------------------------------ | ------------------------------- | ---------------- | ---------------------- |
|
||||
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
|
||||
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
|
||||
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
|
||||
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
|
||||
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
|
||||
| | | | | | | | | |
|
||||
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
|
||||
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
|
||||
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
|
||||
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
|
||||
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
|
||||
|
||||
<details>
|
||||
<summary>表格说明</summary>
|
||||
|
||||
- 所有预训练权重均使用默认设置训练了 300 个周期。Nano (n) 和 Small (s) 模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) 超参数，而 Medium (m)、Large (l) 和 Extra-Large (x) 模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml)。
|
||||
- **mAP<sup>val</sup>** 值表示在 [COCO val2017 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上的单模型、单尺度性能。<br>复现请使用：`python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
- **速度**指标是在 [AWS p3.2xlarge V100 实例](https://aws.amazon.com/ec2/instance-types/p4/)上对 COCO val 图像进行平均测量的。不包括非极大值抑制 (NMS) 时间（约 1 毫秒/图像）。<br>复现请使用：`python val.py --data coco.yaml --img 640 --task speed --batch 1`
|
||||
- **TTA** ([测试时增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)) 包括反射和尺度增强以提高准确性。<br>复现请使用：`python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
||||
|
||||
</details>
|
||||
|
||||
## 🖼️ 分割
|
||||
|
||||
YOLOv5 [v7.0 版本](https://github.com/ultralytics/yolov5/releases/v7.0) 引入了[实例分割](https://docs.ultralytics.com/tasks/segment/)模型，达到了业界顶尖的性能。这些模型设计用于轻松训练、验证和部署。有关完整详细信息，请参阅[发布说明](https://github.com/ultralytics/yolov5/releases/v7.0)，并探索 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb)以获取快速入门示例。
|
||||
|
||||
<details>
|
||||
<summary>分割预训练权重</summary>
|
||||
|
||||
<div align="center">
|
||||
<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
|
||||
<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png" alt="YOLOv5 分割性能图表"></a>
|
||||
</div>
|
||||
|
||||
YOLOv5 分割模型在 [COCO 数据集](https://docs.ultralytics.com/datasets/segment/coco/)上使用 A100 GPU 以 640 像素的图像大小训练了 300 个周期。模型导出为 [ONNX](https://onnx.ai/) FP32 用于 CPU 速度测试，导出为 [TensorRT](https://developer.nvidia.com/tensorrt) FP16 用于 GPU 速度测试。所有速度测试均在 Google [Colab Pro](https://colab.research.google.com/signup) 笔记本上进行，以确保可复现性。
|
||||
|
||||
| 模型 | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时间<br><sup>300 周期<br>A100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TRT A100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ------------------------------------------------------------------------------------------ | ------------------- | -------------------- | --------------------- | ---------------------------------------- | ------------------------------- | ------------------------------- | ---------------- | ---------------------- |
|
||||
| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
|
||||
| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
|
||||
| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
|
||||
| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
|
||||
| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
|
||||
|
||||
- 所有预训练权重均使用 SGD 优化器，`lr0=0.01` 和 `weight_decay=5e-5`，在 640 像素的图像大小下，使用默认设置训练了 300 个周期。<br>训练运行记录在 [https://wandb.ai/glenn-jocher/YOLOv5_v70_official](https://wandb.ai/glenn-jocher/YOLOv5_v70_official)。
|
||||
- **准确度**值表示在 COCO 数据集上的单模型、单尺度性能。<br>复现请使用：`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
|
||||
- **速度**指标是在 [Colab Pro A100 High-RAM 实例](https://colab.research.google.com/signup)上对 100 张推理图像进行平均测量的。值仅表示推理速度（NMS 约增加 1 毫秒/图像）。<br>复现请使用：`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
|
||||
- **导出**到 ONNX (FP32) 和 TensorRT (FP16) 是使用 `export.py` 完成的。<br>复现请使用：`python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>分割使用示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a></summary>
|
||||
|
||||
### 训练
|
||||
|
||||
YOLOv5 分割训练支持通过 `--data coco128-seg.yaml` 参数自动下载 [COCO128-seg 数据集](https://docs.ultralytics.com/datasets/segment/coco8-seg/)。对于完整的 [COCO-segments 数据集](https://docs.ultralytics.com/datasets/segment/coco/)，请使用 `bash data/scripts/get_coco.sh --train --val --segments` 手动下载，然后使用 `python train.py --data coco.yaml` 进行训练。
|
||||
|
||||
```bash
|
||||
# 在单个 GPU 上训练
|
||||
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
|
||||
|
||||
# 使用多 GPU 分布式数据并行 (DDP) 进行训练
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### 验证
|
||||
|
||||
在 COCO 数据集上验证 YOLOv5s-seg 的掩码[平均精度均值 (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map)：
|
||||
|
||||
```bash
|
||||
# 下载 COCO 验证分割集 (780MB, 5000 张图像)
|
||||
bash data/scripts/get_coco.sh --val --segments
|
||||
|
||||
# 验证模型
|
||||
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640
|
||||
```
|
||||
|
||||
### 预测
|
||||
|
||||
使用预训练的 YOLOv5m-seg.pt 模型对 `bus.jpg` 执行分割：
|
||||
|
||||
```bash
|
||||
# 运行预测
|
||||
python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
# 从 PyTorch Hub 加载模型（注意：推理支持可能有所不同）
|
||||
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5m-seg.pt")
|
||||
```
|
||||
|
||||
|  |  |
|
||||
| :-----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: |
|
||||
|
||||
### 导出
|
||||
|
||||
将 YOLOv5s-seg 模型导出为 ONNX 和 TensorRT 格式：
|
||||
|
||||
```bash
|
||||
# 导出模型
|
||||
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## 🏷️ 分类
|
||||
|
||||
YOLOv5 [v6.2 版本](https://github.com/ultralytics/yolov5/releases/v6.2) 引入了对[图像分类](https://docs.ultralytics.com/tasks/classify/)模型训练、验证和部署的支持。请查看[发布说明](https://github.com/ultralytics/yolov5/releases/v6.2)了解详细信息，并参阅 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb)获取快速入门指南。
|
||||
|
||||
<details>
|
||||
<summary>分类预训练权重</summary>
|
||||
|
||||
<br>
|
||||
|
||||
YOLOv5-cls 分类模型在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 上使用 4xA100 实例训练了 90 个周期。[ResNet](https://arxiv.org/abs/1512.03385) 和 [EfficientNet](https://arxiv.org/abs/1905.11946) 模型在相同设置下一起训练以进行比较。模型导出为 [ONNX](https://onnx.ai/) FP32（用于 CPU 速度测试）和 [TensorRT](https://developer.nvidia.com/tensorrt) FP16（用于 GPU 速度测试）。所有速度测试均在 Google [Colab Pro](https://colab.research.google.com/signup) 上运行，以确保可复现性。
|
||||
|
||||
| 模型 | 尺寸<br><sup>(像素) | 准确率<br><sup>top1 | 准确率<br><sup>top5 | 训练<br><sup>90 周期<br>4xA100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TensorRT V100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@224 (B) |
|
||||
| -------------------------------------------------------------------------------------------------- | ------------------- | ------------------- | ------------------- | ------------------------------------- | ------------------------------- | ------------------------------------ | ---------------- | ---------------------- |
|
||||
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
|
||||
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
|
||||
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
|
||||
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
|
||||
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
|
||||
| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
|
||||
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
|
||||
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
|
||||
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
|
||||
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
|
||||
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
|
||||
|
||||
<details>
|
||||
<summary>表格说明（点击展开）</summary>
|
||||
|
||||
- 所有预训练权重均使用 SGD 优化器，`lr0=0.001` 和 `weight_decay=5e-5`，在 224 像素的图像大小下，使用默认设置训练了 90 个周期。<br>训练运行记录在 [https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2](https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2)。
|
||||
- **准确度**值（top-1 和 top-5）表示在 [ImageNet-1k 数据集](https://docs.ultralytics.com/datasets/classify/imagenet/)上的单模型、单尺度性能。<br>复现请使用：`python classify/val.py --data ../datasets/imagenet --img 224`
|
||||
- **速度**指标是在 Google [Colab Pro V100 High-RAM 实例](https://colab.research.google.com/signup)上对 100 张推理图像进行平均测量的。<br>复现请使用：`python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
|
||||
- **导出**到 ONNX (FP32) 和 TensorRT (FP16) 是使用 `export.py` 完成的。<br>复现请使用：`python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
|
||||
|
||||
</details>
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>分类使用示例 <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a></summary>
|
||||
|
||||
### 训练
|
||||
|
||||
YOLOv5 分类训练支持使用 `--data` 参数自动下载诸如 [MNIST](https://docs.ultralytics.com/datasets/classify/mnist/)、[Fashion-MNIST](https://docs.ultralytics.com/datasets/classify/fashion-mnist/)、[CIFAR10](https://docs.ultralytics.com/datasets/classify/cifar10/)、[CIFAR100](https://docs.ultralytics.com/datasets/classify/cifar100/)、[Imagenette](https://docs.ultralytics.com/datasets/classify/imagenette/)、[Imagewoof](https://docs.ultralytics.com/datasets/classify/imagewoof/) 和 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 等数据集。例如，使用 `--data mnist` 开始在 MNIST 上训练。
|
||||
|
||||
```bash
|
||||
# 使用 CIFAR-100 数据集在单个 GPU 上训练
|
||||
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
|
||||
|
||||
# 在 ImageNet 数据集上使用多 GPU DDP 进行训练
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### 验证
|
||||
|
||||
在 ImageNet-1k 验证数据集上验证 YOLOv5m-cls 模型的准确性：
|
||||
|
||||
```bash
|
||||
# 下载 ImageNet 验证集 (6.3GB, 50,000 张图像)
|
||||
bash data/scripts/get_imagenet.sh --val
|
||||
|
||||
# 验证模型
|
||||
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224
|
||||
```
|
||||
|
||||
### 预测
|
||||
|
||||
使用预训练的 YOLOv5s-cls.pt 模型对图像 `bus.jpg` 进行分类：
|
||||
|
||||
```bash
|
||||
# 运行预测
|
||||
python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
# 从 PyTorch Hub 加载模型
|
||||
model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")
|
||||
```
|
||||
|
||||
### 导出
|
||||
|
||||
将训练好的 YOLOv5s-cls、ResNet50 和 EfficientNet_b0 模型导出为 ONNX 和 TensorRT 格式：
|
||||
|
||||
```bash
|
||||
# 导出模型
|
||||
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## ☁️ 环境
|
||||
|
||||
使用我们预配置的环境快速开始。点击下面的图标查看设置详情。
|
||||
|
||||
<div align="center">
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook" title="在 Paperspace Gradient 上运行">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" title="在 Google Colab 中打开">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://www.kaggle.com/models/ultralytics/yolov5" title="在 Kaggle 中打开">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5" title="拉取 Docker 镜像">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/" title="AWS 快速入门指南">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/" title="GCP 快速入门指南">
|
||||
<img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
|
||||
</div>
|
||||
|
||||
## 🤝 贡献
|
||||
|
||||
我们欢迎您的贡献！让 YOLOv5 变得易于访问和有效是社区的共同努力。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing/)开始。通过 [YOLOv5 调查](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)分享您的反馈。感谢所有为使 YOLOv5 变得更好而做出贡献的人！
|
||||
|
||||
[](https://github.com/ultralytics/yolov5/graphs/contributors)
|
||||
|
||||
## 📜 许可证
|
||||
|
||||
Ultralytics 提供两种许可选项以满足不同需求：
|
||||
|
||||
- **AGPL-3.0 许可证**：一种 [OSI 批准的](https://opensource.org/license/agpl-v3)开源许可证，非常适合学术研究、个人项目和测试。它促进开放协作和知识共享。详情请参阅 [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件。
|
||||
- **企业许可证**：专为商业应用量身定制，此许可证允许将 Ultralytics 软件和 AI 模型无缝集成到商业产品和服务中，绕过 AGPL-3.0 的开源要求。对于商业用例，请通过 [Ultralytics 授权许可](https://www.ultralytics.com/license)联系我们。
|
||||
|
||||
## 📧 联系
|
||||
|
||||
对于与 YOLOv5 相关的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。对于一般问题、讨论和社区支持，请加入我们的 [Discord 服务器](https://discord.com/invite/ultralytics)！
|
||||
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
|
||||
<a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
|
||||
</div>
|
||||
290
third_party/yolov5/benchmarks.py
vendored
Normal file
290
third_party/yolov5/benchmarks.py
vendored
Normal file
@ -0,0 +1,290 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Run YOLOv5 benchmarks on all supported export formats.
|
||||
|
||||
Format | `export.py --include` | Model
|
||||
--- | --- | ---
|
||||
PyTorch | - | yolov5s.pt
|
||||
TorchScript | `torchscript` | yolov5s.torchscript
|
||||
ONNX | `onnx` | yolov5s.onnx
|
||||
OpenVINO | `openvino` | yolov5s_openvino_model/
|
||||
TensorRT | `engine` | yolov5s.engine
|
||||
CoreML | `coreml` | yolov5s.mlpackage
|
||||
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
|
||||
TensorFlow GraphDef | `pb` | yolov5s.pb
|
||||
TensorFlow Lite | `tflite` | yolov5s.tflite
|
||||
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
|
||||
TensorFlow.js | `tfjs` | yolov5s_web_model/
|
||||
|
||||
Requirements:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
|
||||
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
|
||||
|
||||
Usage:
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import platform
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# ROOT = ROOT.relative_to(Path.cwd()) # relative
|
||||
|
||||
import export
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import SegmentationModel
|
||||
from segment.val import run as val_seg
|
||||
from utils import notebook_init
|
||||
from utils.general import LOGGER, check_yaml, file_size, print_args
|
||||
from utils.torch_utils import select_device
|
||||
from val import run as val_det
|
||||
|
||||
|
||||
def run(
|
||||
weights=ROOT / "yolov5s.pt", # weights path
|
||||
imgsz=640, # inference size (pixels)
|
||||
batch_size=1, # batch size
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
half=False, # use FP16 half-precision inference
|
||||
test=False, # test exports only
|
||||
pt_only=False, # test PyTorch only
|
||||
hard_fail=False, # throw error on benchmark failure
|
||||
):
|
||||
"""Run YOLOv5 benchmarks on multiple export formats and log results for model performance evaluation.
|
||||
|
||||
Args:
|
||||
weights (Path | str): Path to the model weights file (default: ROOT / "yolov5s.pt").
|
||||
imgsz (int): Inference size in pixels (default: 640).
|
||||
batch_size (int): Batch size for inference (default: 1).
|
||||
data (Path | str): Path to the dataset.yaml file (default: ROOT / "data/coco128.yaml").
|
||||
device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu' (default: "").
|
||||
half (bool): Use FP16 half-precision inference (default: False).
|
||||
test (bool): Test export formats only (default: False).
|
||||
pt_only (bool): Test PyTorch format only (default: False).
|
||||
hard_fail (bool): Throw an error on benchmark failure if True (default: False).
|
||||
|
||||
Returns:
|
||||
None. Logs information about the benchmark results, including the format, size, mAP50-95, and inference time.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
```
|
||||
|
||||
Install required packages:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU support
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU support
|
||||
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
|
||||
|
||||
Run benchmarks:
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
|
||||
Notes:
|
||||
Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML,
|
||||
TensorFlow SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js
|
||||
are unsupported.
|
||||
"""
|
||||
y, t = [], time.time()
|
||||
device = select_device(device)
|
||||
model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc.
|
||||
for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU)
|
||||
try:
|
||||
assert i not in (9, 10), "inference not supported" # Edge TPU and TF.js are unsupported
|
||||
assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13" # CoreML
|
||||
if "cpu" in device.type:
|
||||
assert cpu, "inference not supported on CPU"
|
||||
if "cuda" in device.type:
|
||||
assert gpu, "inference not supported on GPU"
|
||||
|
||||
# Export
|
||||
if f == "-":
|
||||
w = weights # PyTorch format
|
||||
else:
|
||||
w = export.run(
|
||||
weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half
|
||||
)[-1] # all others
|
||||
assert suffix in str(w), "export failed"
|
||||
|
||||
# Validate
|
||||
if model_type == SegmentationModel:
|
||||
result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
|
||||
metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
|
||||
else: # DetectionModel:
|
||||
result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
|
||||
metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls))
|
||||
speed = result[2][1] # times (preprocess, inference, postprocess)
|
||||
y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference
|
||||
except Exception as e:
|
||||
if hard_fail:
|
||||
assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}"
|
||||
LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}")
|
||||
y.append([name, None, None, None]) # mAP, t_inference
|
||||
if pt_only and i == 0:
|
||||
break # break after PyTorch
|
||||
|
||||
# Print results
|
||||
LOGGER.info("\n")
|
||||
parse_opt()
|
||||
notebook_init() # print system info
|
||||
c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""]
|
||||
py = pd.DataFrame(y, columns=c)
|
||||
LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)")
|
||||
LOGGER.info(str(py if map else py.iloc[:, :2]))
|
||||
if hard_fail and isinstance(hard_fail, str):
|
||||
metrics = py["mAP50-95"].array # values to compare to floor
|
||||
floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
|
||||
assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}"
|
||||
return py
|
||||
|
||||
|
||||
def test(
|
||||
weights=ROOT / "yolov5s.pt", # weights path
|
||||
imgsz=640, # inference size (pixels)
|
||||
batch_size=1, # batch size
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
half=False, # use FP16 half-precision inference
|
||||
test=False, # test exports only
|
||||
pt_only=False, # test PyTorch only
|
||||
hard_fail=False, # throw error on benchmark failure
|
||||
):
|
||||
"""Run YOLOv5 export tests for all supported formats and log the results, including export statuses.
|
||||
|
||||
Args:
|
||||
weights (Path | str): Path to the model weights file (.pt format). Default is 'ROOT / "yolov5s.pt"'.
|
||||
imgsz (int): Inference image size (in pixels). Default is 640.
|
||||
batch_size (int): Batch size for testing. Default is 1.
|
||||
data (Path | str): Path to the dataset configuration file (.yaml format). Default is 'ROOT /
|
||||
"data/coco128.yaml"'.
|
||||
device (str): Device for running the tests, can be 'cpu' or a specific CUDA device ('0', '0,1,2,3', etc.).
|
||||
Default is an empty string.
|
||||
half (bool): Use FP16 half-precision for inference if True. Default is False.
|
||||
test (bool): Test export formats only without running inference. Default is False.
|
||||
pt_only (bool): Test only the PyTorch model if True. Default is False.
|
||||
hard_fail (bool): Raise error on export or test failure if True. Default is False.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: DataFrame containing the results of the export tests, including format names and export statuses.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
```
|
||||
|
||||
Install required packages:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU support
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU support
|
||||
$ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT
|
||||
Run export tests:
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
|
||||
Notes:
|
||||
Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow
|
||||
SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js are unsupported.
|
||||
"""
|
||||
y, t = [], time.time()
|
||||
device = select_device(device)
|
||||
for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)
|
||||
try:
|
||||
w = (
|
||||
weights
|
||||
if f == "-"
|
||||
else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]
|
||||
) # weights
|
||||
assert suffix in str(w), "export failed"
|
||||
y.append([name, True])
|
||||
except Exception:
|
||||
y.append([name, False]) # mAP, t_inference
|
||||
|
||||
# Print results
|
||||
LOGGER.info("\n")
|
||||
parse_opt()
|
||||
notebook_init() # print system info
|
||||
py = pd.DataFrame(y, columns=["Format", "Export"])
|
||||
LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)")
|
||||
LOGGER.info(str(py))
|
||||
return py
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses command-line arguments for YOLOv5 model inference configuration.
|
||||
|
||||
Args:
|
||||
weights (str): The path to the weights file. Defaults to 'ROOT / "yolov5s.pt"'.
|
||||
imgsz (int): Inference size in pixels. Defaults to 640.
|
||||
batch_size (int): Batch size. Defaults to 1.
|
||||
data (str): Path to the dataset YAML file. Defaults to 'ROOT / "data/coco128.yaml"'.
|
||||
device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu'. Defaults to an empty string (auto-select).
|
||||
half (bool): Use FP16 half-precision inference. This is a flag and defaults to False.
|
||||
test (bool): Test exports only. This is a flag and defaults to False.
|
||||
pt_only (bool): Test PyTorch only. This is a flag and defaults to False.
|
||||
hard_fail (bool | str): Throw an error on benchmark failure. Can be a boolean or a string representing a minimum
|
||||
metric floor, e.g., '0.29'. Defaults to False.
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: Parsed command-line arguments encapsulated in an argparse Namespace object.
|
||||
|
||||
Notes:
|
||||
The function modifies the 'opt.data' by checking and validating the YAML path using 'check_yaml()'.
|
||||
The parsed arguments are printed for reference using 'print_args()'.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
|
||||
parser.add_argument("--batch-size", type=int, default=1, help="batch size")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--test", action="store_true", help="test exports only")
|
||||
parser.add_argument("--pt-only", action="store_true", help="test PyTorch only")
|
||||
parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric")
|
||||
opt = parser.parse_args()
|
||||
opt.data = check_yaml(opt.data) # check YAML
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 benchmark tests or main training/inference routines based on the provided command-line arguments.
|
||||
|
||||
Args:
|
||||
opt (argparse.Namespace): Parsed command-line arguments including options for weights, image size, batch size,
|
||||
data configuration, device, and other flags for inference settings.
|
||||
|
||||
Returns:
|
||||
None: This function does not return any value. It leverages side-effects such as logging and running benchmarks.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
```
|
||||
|
||||
Notes:
|
||||
- For a complete list of supported export formats and their respective requirements, refer to the
|
||||
[Ultralytics YOLOv5 Export Formats](https://github.com/ultralytics/yolov5#export-formats).
|
||||
- Ensure that you have installed all necessary dependencies by following the installation instructions detailed in
|
||||
the [main repository](https://github.com/ultralytics/yolov5#installation).
|
||||
|
||||
```shell
|
||||
# Running benchmarks on default weights and image size
|
||||
$ python benchmarks.py --weights yolov5s.pt --img 640
|
||||
```
|
||||
"""
|
||||
test(**vars(opt)) if opt.test else run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
241
third_party/yolov5/classify/predict.py
vendored
Normal file
241
third_party/yolov5/classify/predict.py
vendored
Normal file
@ -0,0 +1,241 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
||||
|
||||
Usage - sources:
|
||||
$ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/LNwODJXcvt4' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
|
||||
Usage - formats:
|
||||
$ python classify/predict.py --weights yolov5s-cls.pt # PyTorch
|
||||
yolov5s-cls.torchscript # TorchScript
|
||||
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-cls_openvino_model # OpenVINO
|
||||
yolov5s-cls.engine # TensorRT
|
||||
yolov5s-cls.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-cls_saved_model # TensorFlow SavedModel
|
||||
yolov5s-cls.pb # TensorFlow GraphDef
|
||||
yolov5s-cls.tflite # TensorFlow Lite
|
||||
yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-cls_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from ultralytics.utils.plotting import Annotator
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.augmentations import classify_transforms
|
||||
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
Profile,
|
||||
check_file,
|
||||
check_img_size,
|
||||
check_imshow,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
cv2,
|
||||
increment_path,
|
||||
print_args,
|
||||
strip_optimizer,
|
||||
)
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
weights=ROOT / "yolov5s-cls.pt", # model.pt path(s)
|
||||
source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
imgsz=(224, 224), # inference size (height, width)
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
view_img=False, # show results
|
||||
save_txt=False, # save results to *.txt
|
||||
nosave=False, # do not save images/videos
|
||||
augment=False, # augmented inference
|
||||
visualize=False, # visualize features
|
||||
update=False, # update all models
|
||||
project=ROOT / "runs/predict-cls", # save results to project/name
|
||||
name="exp", # save results to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
vid_stride=1, # video frame-rate stride
|
||||
):
|
||||
"""Conducts YOLOv5 classification inference on diverse input sources and saves results."""
|
||||
source = str(source)
|
||||
save_img = not nosave and not source.endswith(".txt") # save inference images
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
|
||||
webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
|
||||
screenshot = source.lower().startswith("screen")
|
||||
if is_url and is_file:
|
||||
source = check_file(source) # download
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
device = select_device(device)
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
|
||||
# Dataloader
|
||||
bs = 1 # batch_size
|
||||
if webcam:
|
||||
view_img = check_imshow(warn=True)
|
||||
dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
|
||||
bs = len(dataset)
|
||||
elif screenshot:
|
||||
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
|
||||
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||
|
||||
# Run inference
|
||||
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
|
||||
seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
with dt[0]:
|
||||
im = torch.Tensor(im).to(model.device)
|
||||
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
results = model(im)
|
||||
|
||||
# Post-process
|
||||
with dt[2]:
|
||||
pred = F.softmax(results, dim=1) # probabilities
|
||||
|
||||
# Process predictions
|
||||
for i, prob in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if webcam: # batch_size >= 1
|
||||
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||
s += f"{i}: "
|
||||
else:
|
||||
p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
|
||||
|
||||
s += "{:g}x{:g} ".format(*im.shape[2:]) # print string
|
||||
annotator = Annotator(im0, example=str(names), pil=True)
|
||||
|
||||
# Print results
|
||||
top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices
|
||||
s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
|
||||
|
||||
# Write results
|
||||
text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i)
|
||||
if save_img or view_img: # Add bbox to image
|
||||
annotator.text([32, 32], text, txt_color=(255, 255, 255))
|
||||
if save_txt: # Write to file
|
||||
with open(f"{txt_path}.txt", "a") as f:
|
||||
f.write(text + "\n")
|
||||
|
||||
# Stream results
|
||||
im0 = annotator.result()
|
||||
if view_img:
|
||||
if platform.system() == "Linux" and p not in windows:
|
||||
windows.append(p)
|
||||
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||||
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||||
cv2.imshow(str(p), im0)
|
||||
cv2.waitKey(1) # 1 millisecond
|
||||
|
||||
# Save results (image with detections)
|
||||
if save_img:
|
||||
if dataset.mode == "image":
|
||||
cv2.imwrite(save_path, im0)
|
||||
else: # 'video' or 'stream'
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{dt[1].dt * 1e3:.1f}ms")
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
if update:
|
||||
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses command line arguments for YOLOv5 inference settings including model, source, device, and image size."""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model path(s)")
|
||||
parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[224], help="inference size h,w")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--view-img", action="store_true", help="show results")
|
||||
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||||
parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
|
||||
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||||
parser.add_argument("--visualize", action="store_true", help="visualize features")
|
||||
parser.add_argument("--update", action="store_true", help="update all models")
|
||||
parser.add_argument("--project", default=ROOT / "runs/predict-cls", help="save results to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save results to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 model inference with options for ONNX DNN and video frame-rate stride adjustments."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
381
third_party/yolov5/classify/train.py
vendored
Normal file
381
third_party/yolov5/classify/train.py
vendored
Normal file
@ -0,0 +1,381 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Train a YOLOv5 classifier model on a classification dataset.
|
||||
|
||||
Usage - Single-GPU training:
|
||||
$ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
|
||||
|
||||
Usage - Multi-GPU DDP training:
|
||||
$ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
|
||||
|
||||
Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
|
||||
YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
|
||||
Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.hub as hub
|
||||
import torch.optim.lr_scheduler as lr_scheduler
|
||||
import torchvision
|
||||
from torch.cuda import amp
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from classify import val as validate
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import ClassificationModel, DetectionModel
|
||||
from utils.dataloaders import create_classification_dataloader
|
||||
from utils.general import (
|
||||
DATASETS_DIR,
|
||||
LOGGER,
|
||||
TQDM_BAR_FORMAT,
|
||||
WorkingDirectory,
|
||||
check_git_info,
|
||||
check_git_status,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
download,
|
||||
increment_path,
|
||||
init_seeds,
|
||||
print_args,
|
||||
yaml_save,
|
||||
)
|
||||
from utils.loggers import GenericLogger
|
||||
from utils.plots import imshow_cls
|
||||
from utils.torch_utils import (
|
||||
ModelEMA,
|
||||
de_parallel,
|
||||
model_info,
|
||||
reshape_classifier_output,
|
||||
select_device,
|
||||
smart_DDP,
|
||||
smart_optimizer,
|
||||
smartCrossEntropyLoss,
|
||||
torch_distributed_zero_first,
|
||||
)
|
||||
|
||||
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
|
||||
RANK = int(os.getenv("RANK", -1))
|
||||
WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
|
||||
GIT_INFO = check_git_info()
|
||||
|
||||
|
||||
def train(opt, device):
|
||||
"""Trains a YOLOv5 model, managing datasets, model optimization, logging, and saving checkpoints."""
|
||||
init_seeds(opt.seed + 1 + RANK, deterministic=True)
|
||||
save_dir, data, bs, epochs, nw, imgsz, pretrained = (
|
||||
opt.save_dir,
|
||||
Path(opt.data),
|
||||
opt.batch_size,
|
||||
opt.epochs,
|
||||
min(os.cpu_count() - 1, opt.workers),
|
||||
opt.imgsz,
|
||||
str(opt.pretrained).lower() == "true",
|
||||
)
|
||||
cuda = device.type != "cpu"
|
||||
|
||||
# Directories
|
||||
wdir = save_dir / "weights"
|
||||
wdir.mkdir(parents=True, exist_ok=True) # make dir
|
||||
last, best = wdir / "last.pt", wdir / "best.pt"
|
||||
|
||||
# Save run settings
|
||||
yaml_save(save_dir / "opt.yaml", vars(opt))
|
||||
|
||||
# Logger
|
||||
logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
|
||||
|
||||
# Download Dataset
|
||||
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
|
||||
data_dir = data if data.is_dir() else (DATASETS_DIR / data)
|
||||
if not data_dir.is_dir():
|
||||
LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...")
|
||||
t = time.time()
|
||||
if str(data) == "imagenet":
|
||||
subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True)
|
||||
else:
|
||||
url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{data}.zip"
|
||||
download(url, dir=data_dir.parent)
|
||||
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
|
||||
LOGGER.info(s)
|
||||
|
||||
# Dataloaders
|
||||
nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()]) # number of classes
|
||||
trainloader = create_classification_dataloader(
|
||||
path=data_dir / "train",
|
||||
imgsz=imgsz,
|
||||
batch_size=bs // WORLD_SIZE,
|
||||
augment=True,
|
||||
cache=opt.cache,
|
||||
rank=LOCAL_RANK,
|
||||
workers=nw,
|
||||
)
|
||||
|
||||
test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val" # data/test or data/val
|
||||
if RANK in {-1, 0}:
|
||||
testloader = create_classification_dataloader(
|
||||
path=test_dir,
|
||||
imgsz=imgsz,
|
||||
batch_size=bs // WORLD_SIZE * 2,
|
||||
augment=False,
|
||||
cache=opt.cache,
|
||||
rank=-1,
|
||||
workers=nw,
|
||||
)
|
||||
|
||||
# Model
|
||||
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
|
||||
if Path(opt.model).is_file() or opt.model.endswith(".pt"):
|
||||
model = attempt_load(opt.model, device="cpu", fuse=False)
|
||||
elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
|
||||
model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None)
|
||||
else:
|
||||
m = hub.list("ultralytics/yolov5") # + hub.list('pytorch/vision') # models
|
||||
raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m))
|
||||
if isinstance(model, DetectionModel):
|
||||
LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
|
||||
model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model
|
||||
reshape_classifier_output(model, nc) # update class count
|
||||
for m in model.modules():
|
||||
if not pretrained and hasattr(m, "reset_parameters"):
|
||||
m.reset_parameters()
|
||||
if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
|
||||
m.p = opt.dropout # set dropout
|
||||
for p in model.parameters():
|
||||
p.requires_grad = True # for training
|
||||
model = model.to(device)
|
||||
|
||||
# Info
|
||||
if RANK in {-1, 0}:
|
||||
model.names = trainloader.dataset.classes # attach class names
|
||||
model.transforms = testloader.dataset.torch_transforms # attach inference transforms
|
||||
model_info(model)
|
||||
if opt.verbose:
|
||||
LOGGER.info(model)
|
||||
images, labels = next(iter(trainloader))
|
||||
file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg")
|
||||
logger.log_images(file, name="Train Examples")
|
||||
logger.log_graph(model, imgsz) # log model
|
||||
|
||||
# Optimizer
|
||||
optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)
|
||||
|
||||
# Scheduler
|
||||
lrf = 0.01 # final lr (fraction of lr0)
|
||||
|
||||
# lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine
|
||||
def lf(x):
|
||||
"""Linear learning rate scheduler function, scaling learning rate from initial value to `lrf` over `epochs`."""
|
||||
return (1 - x / epochs) * (1 - lrf) + lrf # linear
|
||||
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
|
||||
# scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
|
||||
# final_div_factor=1 / 25 / lrf)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if RANK in {-1, 0} else None
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = smart_DDP(model)
|
||||
|
||||
# Train
|
||||
t0 = time.time()
|
||||
criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function
|
||||
best_fitness = 0.0
|
||||
scaler = amp.GradScaler(enabled=cuda)
|
||||
val = test_dir.stem # 'val' or 'test'
|
||||
LOGGER.info(
|
||||
f"Image sizes {imgsz} train, {imgsz} test\n"
|
||||
f"Using {nw * WORLD_SIZE} dataloader workers\n"
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n"
|
||||
f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
|
||||
)
|
||||
for epoch in range(epochs): # loop over the dataset multiple times
|
||||
tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness
|
||||
model.train()
|
||||
if RANK != -1:
|
||||
trainloader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(trainloader)
|
||||
if RANK in {-1, 0}:
|
||||
pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)
|
||||
for i, (images, labels) in pbar: # progress bar
|
||||
images, labels = images.to(device, non_blocking=True), labels.to(device)
|
||||
|
||||
# Forward
|
||||
with amp.autocast(enabled=cuda): # stability issues when enabled
|
||||
loss = criterion(model(images), labels)
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize
|
||||
scaler.unscale_(optimizer) # unscale gradients
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
|
||||
if RANK in {-1, 0}:
|
||||
# Print
|
||||
tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses
|
||||
mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0) # (GB)
|
||||
pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36
|
||||
|
||||
# Test
|
||||
if i == len(pbar) - 1: # last batch
|
||||
top1, top5, vloss = validate.run(
|
||||
model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar
|
||||
) # test accuracy, loss
|
||||
fitness = top1 # define fitness as top1 accuracy
|
||||
|
||||
# Scheduler
|
||||
scheduler.step()
|
||||
|
||||
# Log metrics
|
||||
if RANK in {-1, 0}:
|
||||
# Best fitness
|
||||
if fitness > best_fitness:
|
||||
best_fitness = fitness
|
||||
|
||||
# Log
|
||||
metrics = {
|
||||
"train/loss": tloss,
|
||||
f"{val}/loss": vloss,
|
||||
"metrics/accuracy_top1": top1,
|
||||
"metrics/accuracy_top5": top5,
|
||||
"lr/0": optimizer.param_groups[0]["lr"],
|
||||
} # learning rate
|
||||
logger.log_metrics(metrics, epoch)
|
||||
|
||||
# Save model
|
||||
final_epoch = epoch + 1 == epochs
|
||||
if (not opt.nosave) or final_epoch:
|
||||
ckpt = {
|
||||
"epoch": epoch,
|
||||
"best_fitness": best_fitness,
|
||||
"model": deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(),
|
||||
"ema": None, # deepcopy(ema.ema).half(),
|
||||
"updates": ema.updates,
|
||||
"optimizer": None, # optimizer.state_dict(),
|
||||
"opt": vars(opt),
|
||||
"git": GIT_INFO, # {remote, branch, commit} if a git repo
|
||||
"date": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fitness:
|
||||
torch.save(ckpt, best)
|
||||
del ckpt
|
||||
|
||||
# Train complete
|
||||
if RANK in {-1, 0} and final_epoch:
|
||||
LOGGER.info(
|
||||
f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)"
|
||||
f"\nResults saved to {colorstr('bold', save_dir)}"
|
||||
f"\nPredict: python classify/predict.py --weights {best} --source im.jpg"
|
||||
f"\nValidate: python classify/val.py --weights {best} --data {data_dir}"
|
||||
f"\nExport: python export.py --weights {best} --include onnx"
|
||||
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
|
||||
f"\nVisualize: https://netron.app\n"
|
||||
)
|
||||
|
||||
# Plot examples
|
||||
images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels
|
||||
pred = torch.max(ema.ema(images.to(device)), 1)[1]
|
||||
file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg")
|
||||
|
||||
# Log results
|
||||
meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
|
||||
logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch)
|
||||
logger.log_model(best, epochs, metadata=meta)
|
||||
|
||||
|
||||
def parse_opt(known=False):
|
||||
"""Parses command line arguments for YOLOv5 training including model path, dataset, epochs, and more, returning
|
||||
parsed arguments.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path")
|
||||
parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...")
|
||||
parser.add_argument("--epochs", type=int, default=10, help="total training epochs")
|
||||
parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)")
|
||||
parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
|
||||
parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"')
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False")
|
||||
parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer")
|
||||
parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate")
|
||||
parser.add_argument("--decay", type=float, default=5e-5, help="weight decay")
|
||||
parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon")
|
||||
parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head")
|
||||
parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)")
|
||||
parser.add_argument("--verbose", action="store_true", help="Verbose mode")
|
||||
parser.add_argument("--seed", type=int, default=0, help="Global training seed")
|
||||
parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 training with given options, handling device setup and DDP mode; includes pre-training checks."""
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
check_git_status()
|
||||
check_requirements(ROOT / "requirements.txt")
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size"
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device("cuda", LOCAL_RANK)
|
||||
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
|
||||
|
||||
# Parameters
|
||||
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
|
||||
|
||||
# Train
|
||||
train(opt, device)
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
"""Executes YOLOv5 model training or inference with specified parameters, returning updated options.
|
||||
|
||||
Example: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
|
||||
"""
|
||||
opt = parse_opt(True)
|
||||
for k, v in kwargs.items():
|
||||
setattr(opt, k, v)
|
||||
main(opt)
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
1485
third_party/yolov5/classify/tutorial.ipynb
vendored
Normal file
1485
third_party/yolov5/classify/tutorial.ipynb
vendored
Normal file
File diff suppressed because it is too large
Load Diff
178
third_party/yolov5/classify/val.py
vendored
Normal file
178
third_party/yolov5/classify/val.py
vendored
Normal file
@ -0,0 +1,178 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Validate a trained YOLOv5 classification model on a classification dataset.
|
||||
|
||||
Usage:
|
||||
$ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
|
||||
$ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet
|
||||
|
||||
Usage - formats:
|
||||
$ python classify/val.py --weights yolov5s-cls.pt # PyTorch
|
||||
yolov5s-cls.torchscript # TorchScript
|
||||
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-cls_openvino_model # OpenVINO
|
||||
yolov5s-cls.engine # TensorRT
|
||||
yolov5s-cls.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-cls_saved_model # TensorFlow SavedModel
|
||||
yolov5s-cls.pb # TensorFlow GraphDef
|
||||
yolov5s-cls.tflite # TensorFlow Lite
|
||||
yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-cls_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.dataloaders import create_classification_dataloader
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
TQDM_BAR_FORMAT,
|
||||
Profile,
|
||||
check_img_size,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
increment_path,
|
||||
print_args,
|
||||
)
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
data=ROOT / "../datasets/mnist", # dataset dir
|
||||
weights=ROOT / "yolov5s-cls.pt", # model.pt path(s)
|
||||
batch_size=128, # batch size
|
||||
imgsz=224, # inference size (pixels)
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
workers=8, # max dataloader workers (per RANK in DDP mode)
|
||||
verbose=False, # verbose output
|
||||
project=ROOT / "runs/val-cls", # save to project/name
|
||||
name="exp", # save to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
model=None,
|
||||
dataloader=None,
|
||||
criterion=None,
|
||||
pbar=None,
|
||||
):
|
||||
"""Validates a YOLOv5 classification model on a dataset, computing metrics like top1 and top5 accuracy."""
|
||||
# Initialize/load model and set device
|
||||
training = model is not None
|
||||
if training: # called by train.py
|
||||
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
|
||||
half &= device.type != "cpu" # half precision only supported on CUDA
|
||||
model.half() if half else model.float()
|
||||
else: # called directly
|
||||
device = select_device(device, batch_size=batch_size)
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
save_dir.mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
|
||||
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
half = model.fp16 # FP16 supported on limited backends with CUDA
|
||||
if engine:
|
||||
batch_size = model.batch_size
|
||||
else:
|
||||
device = model.device
|
||||
if not (pt or jit):
|
||||
batch_size = 1 # export.py models default to batch-size 1
|
||||
LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
|
||||
|
||||
# Dataloader
|
||||
data = Path(data)
|
||||
test_dir = data / "test" if (data / "test").exists() else data / "val" # data/test or data/val
|
||||
dataloader = create_classification_dataloader(
|
||||
path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers
|
||||
)
|
||||
|
||||
model.eval()
|
||||
pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device))
|
||||
n = len(dataloader) # number of batches
|
||||
action = "validating" if dataloader.dataset.root.stem == "val" else "testing"
|
||||
desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
|
||||
bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
|
||||
with torch.cuda.amp.autocast(enabled=device.type != "cpu"):
|
||||
for images, labels in bar:
|
||||
with dt[0]:
|
||||
images, labels = images.to(device, non_blocking=True), labels.to(device)
|
||||
|
||||
with dt[1]:
|
||||
y = model(images)
|
||||
|
||||
with dt[2]:
|
||||
pred.append(y.argsort(1, descending=True)[:, :5])
|
||||
targets.append(labels)
|
||||
if criterion:
|
||||
loss += criterion(y, labels)
|
||||
|
||||
loss /= n
|
||||
pred, targets = torch.cat(pred), torch.cat(targets)
|
||||
correct = (targets[:, None] == pred).float()
|
||||
acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
|
||||
top1, top5 = acc.mean(0).tolist()
|
||||
|
||||
if pbar:
|
||||
pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
|
||||
if verbose: # all classes
|
||||
LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
|
||||
LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
|
||||
for i, c in model.names.items():
|
||||
acc_i = acc[targets == i]
|
||||
top1i, top5i = acc_i.mean(0).tolist()
|
||||
LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt) # speeds per image
|
||||
shape = (1, 3, imgsz, imgsz)
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t)
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
||||
|
||||
return top1, top5, loss
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses and returns command line arguments for YOLOv5 model evaluation and inference settings."""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path")
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)")
|
||||
parser.add_argument("--batch-size", type=int, default=128, help="batch size")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output")
|
||||
parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
opt = parser.parse_args()
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes the YOLOv5 model prediction workflow, handling argument parsing and requirement checks."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
73
third_party/yolov5/data/Argoverse.yaml
vendored
Normal file
73
third_party/yolov5/data/Argoverse.yaml
vendored
Normal file
@ -0,0 +1,73 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
|
||||
# Example usage: python train.py --data Argoverse.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── Argoverse ← downloads here (31.3 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Argoverse # dataset root dir
|
||||
train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
|
||||
val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: bus
|
||||
5: truck
|
||||
6: traffic_light
|
||||
7: stop_sign
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
|
||||
from tqdm import tqdm
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
def argoverse2yolo(set):
|
||||
labels = {}
|
||||
a = json.load(open(set, "rb"))
|
||||
for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
|
||||
img_id = annot['image_id']
|
||||
img_name = a['images'][img_id]['name']
|
||||
img_label_name = f'{img_name[:-3]}txt'
|
||||
|
||||
cls = annot['category_id'] # instance class id
|
||||
x_center, y_center, width, height = annot['bbox']
|
||||
x_center = (x_center + width / 2) / 1920.0 # offset and scale
|
||||
y_center = (y_center + height / 2) / 1200.0 # offset and scale
|
||||
width /= 1920.0 # scale
|
||||
height /= 1200.0 # scale
|
||||
|
||||
img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
|
||||
if not img_dir.exists():
|
||||
img_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
k = str(img_dir / img_label_name)
|
||||
if k not in labels:
|
||||
labels[k] = []
|
||||
labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
|
||||
|
||||
for k in labels:
|
||||
with open(k, "w") as f:
|
||||
f.writelines(labels[k])
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
|
||||
download(urls, dir=dir, delete=False)
|
||||
|
||||
# Convert
|
||||
annotations_dir = 'Argoverse-HD/annotations/'
|
||||
(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
|
||||
for d in "train.json", "val.json":
|
||||
argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
|
||||
53
third_party/yolov5/data/GlobalWheat2020.yaml
vendored
Normal file
53
third_party/yolov5/data/GlobalWheat2020.yaml
vendored
Normal file
@ -0,0 +1,53 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
|
||||
# Example usage: python train.py --data GlobalWheat2020.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── GlobalWheat2020 ← downloads here (7.0 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/GlobalWheat2020 # dataset root dir
|
||||
train: # train images (relative to 'path') 3422 images
|
||||
- images/arvalis_1
|
||||
- images/arvalis_2
|
||||
- images/arvalis_3
|
||||
- images/ethz_1
|
||||
- images/rres_1
|
||||
- images/inrae_1
|
||||
- images/usask_1
|
||||
val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
|
||||
- images/ethz_1
|
||||
test: # test images (optional) 1276 images
|
||||
- images/utokyo_1
|
||||
- images/utokyo_2
|
||||
- images/nau_1
|
||||
- images/uq_1
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: wheat_head
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
|
||||
download(urls, dir=dir)
|
||||
|
||||
# Make Directories
|
||||
for p in 'annotations', 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Move
|
||||
for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
|
||||
'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
|
||||
(dir / p).rename(dir / 'images' / p) # move to /images
|
||||
f = (dir / p).with_suffix('.json') # json file
|
||||
if f.exists():
|
||||
f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
|
||||
1021
third_party/yolov5/data/ImageNet.yaml
vendored
Normal file
1021
third_party/yolov5/data/ImageNet.yaml
vendored
Normal file
File diff suppressed because it is too large
Load Diff
31
third_party/yolov5/data/ImageNet10.yaml
vendored
Normal file
31
third_party/yolov5/data/ImageNet10.yaml
vendored
Normal file
@ -0,0 +1,31 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
|
||||
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
|
||||
# Example usage: python classify/train.py --data imagenet
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet10 ← downloads here
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/imagenet10 # dataset root dir
|
||||
train: train # train images (relative to 'path') 1281167 images
|
||||
val: val # val images (relative to 'path') 50000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: tench
|
||||
1: goldfish
|
||||
2: great white shark
|
||||
3: tiger shark
|
||||
4: hammerhead shark
|
||||
5: electric ray
|
||||
6: stingray
|
||||
7: cock
|
||||
8: hen
|
||||
9: ostrich
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: data/scripts/get_imagenet10.sh
|
||||
120
third_party/yolov5/data/ImageNet100.yaml
vendored
Normal file
120
third_party/yolov5/data/ImageNet100.yaml
vendored
Normal file
@ -0,0 +1,120 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
|
||||
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
|
||||
# Example usage: python classify/train.py --data imagenet
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet100 ← downloads here
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/imagenet100 # dataset root dir
|
||||
train: train # train images (relative to 'path') 1281167 images
|
||||
val: val # val images (relative to 'path') 50000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: tench
|
||||
1: goldfish
|
||||
2: great white shark
|
||||
3: tiger shark
|
||||
4: hammerhead shark
|
||||
5: electric ray
|
||||
6: stingray
|
||||
7: cock
|
||||
8: hen
|
||||
9: ostrich
|
||||
10: brambling
|
||||
11: goldfinch
|
||||
12: house finch
|
||||
13: junco
|
||||
14: indigo bunting
|
||||
15: American robin
|
||||
16: bulbul
|
||||
17: jay
|
||||
18: magpie
|
||||
19: chickadee
|
||||
20: American dipper
|
||||
21: kite
|
||||
22: bald eagle
|
||||
23: vulture
|
||||
24: great grey owl
|
||||
25: fire salamander
|
||||
26: smooth newt
|
||||
27: newt
|
||||
28: spotted salamander
|
||||
29: axolotl
|
||||
30: American bullfrog
|
||||
31: tree frog
|
||||
32: tailed frog
|
||||
33: loggerhead sea turtle
|
||||
34: leatherback sea turtle
|
||||
35: mud turtle
|
||||
36: terrapin
|
||||
37: box turtle
|
||||
38: banded gecko
|
||||
39: green iguana
|
||||
40: Carolina anole
|
||||
41: desert grassland whiptail lizard
|
||||
42: agama
|
||||
43: frilled-necked lizard
|
||||
44: alligator lizard
|
||||
45: Gila monster
|
||||
46: European green lizard
|
||||
47: chameleon
|
||||
48: Komodo dragon
|
||||
49: Nile crocodile
|
||||
50: American alligator
|
||||
51: triceratops
|
||||
52: worm snake
|
||||
53: ring-necked snake
|
||||
54: eastern hog-nosed snake
|
||||
55: smooth green snake
|
||||
56: kingsnake
|
||||
57: garter snake
|
||||
58: water snake
|
||||
59: vine snake
|
||||
60: night snake
|
||||
61: boa constrictor
|
||||
62: African rock python
|
||||
63: Indian cobra
|
||||
64: green mamba
|
||||
65: sea snake
|
||||
66: Saharan horned viper
|
||||
67: eastern diamondback rattlesnake
|
||||
68: sidewinder
|
||||
69: trilobite
|
||||
70: harvestman
|
||||
71: scorpion
|
||||
72: yellow garden spider
|
||||
73: barn spider
|
||||
74: European garden spider
|
||||
75: southern black widow
|
||||
76: tarantula
|
||||
77: wolf spider
|
||||
78: tick
|
||||
79: centipede
|
||||
80: black grouse
|
||||
81: ptarmigan
|
||||
82: ruffed grouse
|
||||
83: prairie grouse
|
||||
84: peacock
|
||||
85: quail
|
||||
86: partridge
|
||||
87: grey parrot
|
||||
88: macaw
|
||||
89: sulphur-crested cockatoo
|
||||
90: lorikeet
|
||||
91: coucal
|
||||
92: bee eater
|
||||
93: hornbill
|
||||
94: hummingbird
|
||||
95: jacamar
|
||||
96: toucan
|
||||
97: duck
|
||||
98: red-breasted merganser
|
||||
99: goose
|
||||
# Download script/URL (optional)
|
||||
download: data/scripts/get_imagenet100.sh
|
||||
1021
third_party/yolov5/data/ImageNet1000.yaml
vendored
Normal file
1021
third_party/yolov5/data/ImageNet1000.yaml
vendored
Normal file
File diff suppressed because it is too large
Load Diff
437
third_party/yolov5/data/Objects365.yaml
vendored
Normal file
437
third_party/yolov5/data/Objects365.yaml
vendored
Normal file
@ -0,0 +1,437 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Objects365 dataset https://www.objects365.org/ by Megvii
|
||||
# Example usage: python train.py --data Objects365.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Objects365 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1742289 images
|
||||
val: images/val # val images (relative to 'path') 80000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: Person
|
||||
1: Sneakers
|
||||
2: Chair
|
||||
3: Other Shoes
|
||||
4: Hat
|
||||
5: Car
|
||||
6: Lamp
|
||||
7: Glasses
|
||||
8: Bottle
|
||||
9: Desk
|
||||
10: Cup
|
||||
11: Street Lights
|
||||
12: Cabinet/shelf
|
||||
13: Handbag/Satchel
|
||||
14: Bracelet
|
||||
15: Plate
|
||||
16: Picture/Frame
|
||||
17: Helmet
|
||||
18: Book
|
||||
19: Gloves
|
||||
20: Storage box
|
||||
21: Boat
|
||||
22: Leather Shoes
|
||||
23: Flower
|
||||
24: Bench
|
||||
25: Potted Plant
|
||||
26: Bowl/Basin
|
||||
27: Flag
|
||||
28: Pillow
|
||||
29: Boots
|
||||
30: Vase
|
||||
31: Microphone
|
||||
32: Necklace
|
||||
33: Ring
|
||||
34: SUV
|
||||
35: Wine Glass
|
||||
36: Belt
|
||||
37: Monitor/TV
|
||||
38: Backpack
|
||||
39: Umbrella
|
||||
40: Traffic Light
|
||||
41: Speaker
|
||||
42: Watch
|
||||
43: Tie
|
||||
44: Trash bin Can
|
||||
45: Slippers
|
||||
46: Bicycle
|
||||
47: Stool
|
||||
48: Barrel/bucket
|
||||
49: Van
|
||||
50: Couch
|
||||
51: Sandals
|
||||
52: Basket
|
||||
53: Drum
|
||||
54: Pen/Pencil
|
||||
55: Bus
|
||||
56: Wild Bird
|
||||
57: High Heels
|
||||
58: Motorcycle
|
||||
59: Guitar
|
||||
60: Carpet
|
||||
61: Cell Phone
|
||||
62: Bread
|
||||
63: Camera
|
||||
64: Canned
|
||||
65: Truck
|
||||
66: Traffic cone
|
||||
67: Cymbal
|
||||
68: Lifesaver
|
||||
69: Towel
|
||||
70: Stuffed Toy
|
||||
71: Candle
|
||||
72: Sailboat
|
||||
73: Laptop
|
||||
74: Awning
|
||||
75: Bed
|
||||
76: Faucet
|
||||
77: Tent
|
||||
78: Horse
|
||||
79: Mirror
|
||||
80: Power outlet
|
||||
81: Sink
|
||||
82: Apple
|
||||
83: Air Conditioner
|
||||
84: Knife
|
||||
85: Hockey Stick
|
||||
86: Paddle
|
||||
87: Pickup Truck
|
||||
88: Fork
|
||||
89: Traffic Sign
|
||||
90: Balloon
|
||||
91: Tripod
|
||||
92: Dog
|
||||
93: Spoon
|
||||
94: Clock
|
||||
95: Pot
|
||||
96: Cow
|
||||
97: Cake
|
||||
98: Dinning Table
|
||||
99: Sheep
|
||||
100: Hanger
|
||||
101: Blackboard/Whiteboard
|
||||
102: Napkin
|
||||
103: Other Fish
|
||||
104: Orange/Tangerine
|
||||
105: Toiletry
|
||||
106: Keyboard
|
||||
107: Tomato
|
||||
108: Lantern
|
||||
109: Machinery Vehicle
|
||||
110: Fan
|
||||
111: Green Vegetables
|
||||
112: Banana
|
||||
113: Baseball Glove
|
||||
114: Airplane
|
||||
115: Mouse
|
||||
116: Train
|
||||
117: Pumpkin
|
||||
118: Soccer
|
||||
119: Skiboard
|
||||
120: Luggage
|
||||
121: Nightstand
|
||||
122: Tea pot
|
||||
123: Telephone
|
||||
124: Trolley
|
||||
125: Head Phone
|
||||
126: Sports Car
|
||||
127: Stop Sign
|
||||
128: Dessert
|
||||
129: Scooter
|
||||
130: Stroller
|
||||
131: Crane
|
||||
132: Remote
|
||||
133: Refrigerator
|
||||
134: Oven
|
||||
135: Lemon
|
||||
136: Duck
|
||||
137: Baseball Bat
|
||||
138: Surveillance Camera
|
||||
139: Cat
|
||||
140: Jug
|
||||
141: Broccoli
|
||||
142: Piano
|
||||
143: Pizza
|
||||
144: Elephant
|
||||
145: Skateboard
|
||||
146: Surfboard
|
||||
147: Gun
|
||||
148: Skating and Skiing shoes
|
||||
149: Gas stove
|
||||
150: Donut
|
||||
151: Bow Tie
|
||||
152: Carrot
|
||||
153: Toilet
|
||||
154: Kite
|
||||
155: Strawberry
|
||||
156: Other Balls
|
||||
157: Shovel
|
||||
158: Pepper
|
||||
159: Computer Box
|
||||
160: Toilet Paper
|
||||
161: Cleaning Products
|
||||
162: Chopsticks
|
||||
163: Microwave
|
||||
164: Pigeon
|
||||
165: Baseball
|
||||
166: Cutting/chopping Board
|
||||
167: Coffee Table
|
||||
168: Side Table
|
||||
169: Scissors
|
||||
170: Marker
|
||||
171: Pie
|
||||
172: Ladder
|
||||
173: Snowboard
|
||||
174: Cookies
|
||||
175: Radiator
|
||||
176: Fire Hydrant
|
||||
177: Basketball
|
||||
178: Zebra
|
||||
179: Grape
|
||||
180: Giraffe
|
||||
181: Potato
|
||||
182: Sausage
|
||||
183: Tricycle
|
||||
184: Violin
|
||||
185: Egg
|
||||
186: Fire Extinguisher
|
||||
187: Candy
|
||||
188: Fire Truck
|
||||
189: Billiards
|
||||
190: Converter
|
||||
191: Bathtub
|
||||
192: Wheelchair
|
||||
193: Golf Club
|
||||
194: Briefcase
|
||||
195: Cucumber
|
||||
196: Cigar/Cigarette
|
||||
197: Paint Brush
|
||||
198: Pear
|
||||
199: Heavy Truck
|
||||
200: Hamburger
|
||||
201: Extractor
|
||||
202: Extension Cord
|
||||
203: Tong
|
||||
204: Tennis Racket
|
||||
205: Folder
|
||||
206: American Football
|
||||
207: earphone
|
||||
208: Mask
|
||||
209: Kettle
|
||||
210: Tennis
|
||||
211: Ship
|
||||
212: Swing
|
||||
213: Coffee Machine
|
||||
214: Slide
|
||||
215: Carriage
|
||||
216: Onion
|
||||
217: Green beans
|
||||
218: Projector
|
||||
219: Frisbee
|
||||
220: Washing Machine/Drying Machine
|
||||
221: Chicken
|
||||
222: Printer
|
||||
223: Watermelon
|
||||
224: Saxophone
|
||||
225: Tissue
|
||||
226: Toothbrush
|
||||
227: Ice cream
|
||||
228: Hot-air balloon
|
||||
229: Cello
|
||||
230: French Fries
|
||||
231: Scale
|
||||
232: Trophy
|
||||
233: Cabbage
|
||||
234: Hot dog
|
||||
235: Blender
|
||||
236: Peach
|
||||
237: Rice
|
||||
238: Wallet/Purse
|
||||
239: Volleyball
|
||||
240: Deer
|
||||
241: Goose
|
||||
242: Tape
|
||||
243: Tablet
|
||||
244: Cosmetics
|
||||
245: Trumpet
|
||||
246: Pineapple
|
||||
247: Golf Ball
|
||||
248: Ambulance
|
||||
249: Parking meter
|
||||
250: Mango
|
||||
251: Key
|
||||
252: Hurdle
|
||||
253: Fishing Rod
|
||||
254: Medal
|
||||
255: Flute
|
||||
256: Brush
|
||||
257: Penguin
|
||||
258: Megaphone
|
||||
259: Corn
|
||||
260: Lettuce
|
||||
261: Garlic
|
||||
262: Swan
|
||||
263: Helicopter
|
||||
264: Green Onion
|
||||
265: Sandwich
|
||||
266: Nuts
|
||||
267: Speed Limit Sign
|
||||
268: Induction Cooker
|
||||
269: Broom
|
||||
270: Trombone
|
||||
271: Plum
|
||||
272: Rickshaw
|
||||
273: Goldfish
|
||||
274: Kiwi fruit
|
||||
275: Router/modem
|
||||
276: Poker Card
|
||||
277: Toaster
|
||||
278: Shrimp
|
||||
279: Sushi
|
||||
280: Cheese
|
||||
281: Notepaper
|
||||
282: Cherry
|
||||
283: Pliers
|
||||
284: CD
|
||||
285: Pasta
|
||||
286: Hammer
|
||||
287: Cue
|
||||
288: Avocado
|
||||
289: Hamimelon
|
||||
290: Flask
|
||||
291: Mushroom
|
||||
292: Screwdriver
|
||||
293: Soap
|
||||
294: Recorder
|
||||
295: Bear
|
||||
296: Eggplant
|
||||
297: Board Eraser
|
||||
298: Coconut
|
||||
299: Tape Measure/Ruler
|
||||
300: Pig
|
||||
301: Showerhead
|
||||
302: Globe
|
||||
303: Chips
|
||||
304: Steak
|
||||
305: Crosswalk Sign
|
||||
306: Stapler
|
||||
307: Camel
|
||||
308: Formula 1
|
||||
309: Pomegranate
|
||||
310: Dishwasher
|
||||
311: Crab
|
||||
312: Hoverboard
|
||||
313: Meat ball
|
||||
314: Rice Cooker
|
||||
315: Tuba
|
||||
316: Calculator
|
||||
317: Papaya
|
||||
318: Antelope
|
||||
319: Parrot
|
||||
320: Seal
|
||||
321: Butterfly
|
||||
322: Dumbbell
|
||||
323: Donkey
|
||||
324: Lion
|
||||
325: Urinal
|
||||
326: Dolphin
|
||||
327: Electric Drill
|
||||
328: Hair Dryer
|
||||
329: Egg tart
|
||||
330: Jellyfish
|
||||
331: Treadmill
|
||||
332: Lighter
|
||||
333: Grapefruit
|
||||
334: Game board
|
||||
335: Mop
|
||||
336: Radish
|
||||
337: Baozi
|
||||
338: Target
|
||||
339: French
|
||||
340: Spring Rolls
|
||||
341: Monkey
|
||||
342: Rabbit
|
||||
343: Pencil Case
|
||||
344: Yak
|
||||
345: Red Cabbage
|
||||
346: Binoculars
|
||||
347: Asparagus
|
||||
348: Barbell
|
||||
349: Scallop
|
||||
350: Noddles
|
||||
351: Comb
|
||||
352: Dumpling
|
||||
353: Oyster
|
||||
354: Table Tennis paddle
|
||||
355: Cosmetics Brush/Eyeliner Pencil
|
||||
356: Chainsaw
|
||||
357: Eraser
|
||||
358: Lobster
|
||||
359: Durian
|
||||
360: Okra
|
||||
361: Lipstick
|
||||
362: Cosmetics Mirror
|
||||
363: Curling
|
||||
364: Table Tennis
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.general import Path, check_requirements, download, np, xyxy2xywhn
|
||||
|
||||
check_requirements('pycocotools>=2.0')
|
||||
from pycocotools.coco import COCO
|
||||
|
||||
# Make Directories
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
for p in 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
for q in 'train', 'val':
|
||||
(dir / p / q).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Train, Val Splits
|
||||
for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
|
||||
print(f"Processing {split} in {patches} patches ...")
|
||||
images, labels = dir / 'images' / split, dir / 'labels' / split
|
||||
|
||||
# Download
|
||||
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
|
||||
if split == 'train':
|
||||
download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
|
||||
download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
|
||||
elif split == 'val':
|
||||
download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
|
||||
download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
|
||||
download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
|
||||
|
||||
# Move
|
||||
for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
|
||||
f.rename(images / f.name) # move to /images/{split}
|
||||
|
||||
# Labels
|
||||
coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
|
||||
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
|
||||
for cid, cat in enumerate(names):
|
||||
catIds = coco.getCatIds(catNms=[cat])
|
||||
imgIds = coco.getImgIds(catIds=catIds)
|
||||
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
|
||||
width, height = im["width"], im["height"]
|
||||
path = Path(im["file_name"]) # image filename
|
||||
try:
|
||||
with open(labels / path.with_suffix('.txt').name, 'a') as file:
|
||||
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=False)
|
||||
for a in coco.loadAnns(annIds):
|
||||
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
|
||||
xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
|
||||
x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
|
||||
file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
52
third_party/yolov5/data/SKU-110K.yaml
vendored
Normal file
52
third_party/yolov5/data/SKU-110K.yaml
vendored
Normal file
@ -0,0 +1,52 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
|
||||
# Example usage: python train.py --data SKU-110K.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── SKU-110K ← downloads here (13.6 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/SKU-110K # dataset root dir
|
||||
train: train.txt # train images (relative to 'path') 8219 images
|
||||
val: val.txt # val images (relative to 'path') 588 images
|
||||
test: test.txt # test images (optional) 2936 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: object
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import shutil
|
||||
from tqdm import tqdm
|
||||
from utils.general import np, pd, Path, download, xyxy2xywh
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
parent = Path(dir.parent) # download dir
|
||||
urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
|
||||
download(urls, dir=parent, delete=False)
|
||||
|
||||
# Rename directories
|
||||
if dir.exists():
|
||||
shutil.rmtree(dir)
|
||||
(parent / 'SKU110K_fixed').rename(dir) # rename dir
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
|
||||
|
||||
# Convert labels
|
||||
names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
|
||||
for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
|
||||
x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
|
||||
images, unique_images = x[:, 0], np.unique(x[:, 0])
|
||||
with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
|
||||
f.writelines(f'./images/{s}\n' for s in unique_images)
|
||||
for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
|
||||
cls = 0 # single-class dataset
|
||||
with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
|
||||
for r in x[images == im]:
|
||||
w, h = r[6], r[7] # image width, height
|
||||
xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
|
||||
f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
|
||||
99
third_party/yolov5/data/VOC.yaml
vendored
Normal file
99
third_party/yolov5/data/VOC.yaml
vendored
Normal file
@ -0,0 +1,99 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
|
||||
# Example usage: python train.py --data VOC.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── VOC ← downloads here (2.8 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VOC
|
||||
train: # train images (relative to 'path') 16551 images
|
||||
- images/train2012
|
||||
- images/train2007
|
||||
- images/val2012
|
||||
- images/val2007
|
||||
val: # val images (relative to 'path') 4952 images
|
||||
- images/test2007
|
||||
test: # test images (optional)
|
||||
- images/test2007
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: aeroplane
|
||||
1: bicycle
|
||||
2: bird
|
||||
3: boat
|
||||
4: bottle
|
||||
5: bus
|
||||
6: car
|
||||
7: cat
|
||||
8: chair
|
||||
9: cow
|
||||
10: diningtable
|
||||
11: dog
|
||||
12: horse
|
||||
13: motorbike
|
||||
14: person
|
||||
15: pottedplant
|
||||
16: sheep
|
||||
17: sofa
|
||||
18: train
|
||||
19: tvmonitor
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
from tqdm import tqdm
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
def convert_label(path, lb_path, year, image_id):
|
||||
def convert_box(size, box):
|
||||
dw, dh = 1. / size[0], 1. / size[1]
|
||||
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
|
||||
return x * dw, y * dh, w * dw, h * dh
|
||||
|
||||
in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
|
||||
out_file = open(lb_path, 'w')
|
||||
tree = ET.parse(in_file)
|
||||
root = tree.getroot()
|
||||
size = root.find('size')
|
||||
w = int(size.find('width').text)
|
||||
h = int(size.find('height').text)
|
||||
|
||||
names = list(yaml['names'].values()) # names list
|
||||
for obj in root.iter('object'):
|
||||
cls = obj.find('name').text
|
||||
if cls in names and int(obj.find('difficult').text) != 1:
|
||||
xmlbox = obj.find('bndbox')
|
||||
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
|
||||
cls_id = names.index(cls) # class id
|
||||
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
|
||||
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||
download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
|
||||
|
||||
# Convert
|
||||
path = dir / 'images/VOCdevkit'
|
||||
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
|
||||
imgs_path = dir / 'images' / f'{image_set}{year}'
|
||||
lbs_path = dir / 'labels' / f'{image_set}{year}'
|
||||
imgs_path.mkdir(exist_ok=True, parents=True)
|
||||
lbs_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
|
||||
image_ids = f.read().strip().split()
|
||||
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
|
||||
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
|
||||
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
|
||||
f.rename(imgs_path / f.name) # move image
|
||||
convert_label(path, lb_path, year, id) # convert labels to YOLO format
|
||||
69
third_party/yolov5/data/VisDrone.yaml
vendored
Normal file
69
third_party/yolov5/data/VisDrone.yaml
vendored
Normal file
@ -0,0 +1,69 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
|
||||
# Example usage: python train.py --data VisDrone.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── VisDrone ← downloads here (2.3 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VisDrone # dataset root dir
|
||||
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: pedestrian
|
||||
1: people
|
||||
2: bicycle
|
||||
3: car
|
||||
4: van
|
||||
5: truck
|
||||
6: tricycle
|
||||
7: awning-tricycle
|
||||
8: bus
|
||||
9: motor
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from utils.general import download, os, Path
|
||||
|
||||
def visdrone2yolo(dir):
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
def convert_box(size, box):
|
||||
# Convert VisDrone box to YOLO xywh box
|
||||
dw = 1. / size[0]
|
||||
dh = 1. / size[1]
|
||||
return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
|
||||
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
|
||||
pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
|
||||
for f in pbar:
|
||||
img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
|
||||
lines = []
|
||||
with open(f, 'r') as file: # read annotation.txt
|
||||
for row in [x.split(',') for x in file.read().strip().splitlines()]:
|
||||
if row[4] == '0': # VisDrone 'ignored regions' class 0
|
||||
continue
|
||||
cls = int(row[5]) - 1
|
||||
box = convert_box(img_size, tuple(map(int, row[:4])))
|
||||
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
|
||||
with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
|
||||
fl.writelines(lines) # write label.txt
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']
|
||||
download(urls, dir=dir, curl=True, threads=4)
|
||||
|
||||
# Convert
|
||||
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
|
||||
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
|
||||
115
third_party/yolov5/data/coco.yaml
vendored
Normal file
115
third_party/yolov5/data/coco.yaml
vendored
Normal file
@ -0,0 +1,115 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# COCO 2017 dataset http://cocodataset.org by Microsoft
|
||||
# Example usage: python train.py --data coco.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco ← downloads here (20.1 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: |
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
# Download labels
|
||||
segments = False # segment or box labels
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
|
||||
urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
|
||||
download(urls, dir=dir.parent)
|
||||
|
||||
# Download data
|
||||
urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
|
||||
'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
|
||||
'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
|
||||
download(urls, dir=dir / 'images', threads=3)
|
||||
100
third_party/yolov5/data/coco128-seg.yaml
vendored
Normal file
100
third_party/yolov5/data/coco128-seg.yaml
vendored
Normal file
@ -0,0 +1,100 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Example usage: python train.py --data coco128.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco128-seg ← downloads here (7 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco128-seg # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip
|
||||
100
third_party/yolov5/data/coco128.yaml
vendored
Normal file
100
third_party/yolov5/data/coco128.yaml
vendored
Normal file
@ -0,0 +1,100 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Example usage: python train.py --data coco128.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here (7 MB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco128 # dataset root dir
|
||||
train: images/train2017 # train images (relative to 'path') 128 images
|
||||
val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip
|
||||
35
third_party/yolov5/data/hyps/hyp.Objects365.yaml
vendored
Normal file
35
third_party/yolov5/data/hyps/hyp.Objects365.yaml
vendored
Normal file
@ -0,0 +1,35 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Hyperparameters for Objects365 training
|
||||
# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
|
||||
# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.00258
|
||||
lrf: 0.17
|
||||
momentum: 0.779
|
||||
weight_decay: 0.00058
|
||||
warmup_epochs: 1.33
|
||||
warmup_momentum: 0.86
|
||||
warmup_bias_lr: 0.0711
|
||||
box: 0.0539
|
||||
cls: 0.299
|
||||
cls_pw: 0.825
|
||||
obj: 0.632
|
||||
obj_pw: 1.0
|
||||
iou_t: 0.2
|
||||
anchor_t: 3.44
|
||||
anchors: 3.2
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.0188
|
||||
hsv_s: 0.704
|
||||
hsv_v: 0.36
|
||||
degrees: 0.0
|
||||
translate: 0.0902
|
||||
scale: 0.491
|
||||
shear: 0.0
|
||||
perspective: 0.0
|
||||
flipud: 0.0
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.0
|
||||
copy_paste: 0.0
|
||||
41
third_party/yolov5/data/hyps/hyp.VOC.yaml
vendored
Normal file
41
third_party/yolov5/data/hyps/hyp.VOC.yaml
vendored
Normal file
@ -0,0 +1,41 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Hyperparameters for VOC training
|
||||
# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
|
||||
# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
# YOLOv5 Hyperparameter Evolution Results
|
||||
# Best generation: 467
|
||||
# Last generation: 996
|
||||
# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss
|
||||
# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865
|
||||
|
||||
lr0: 0.00334
|
||||
lrf: 0.15135
|
||||
momentum: 0.74832
|
||||
weight_decay: 0.00025
|
||||
warmup_epochs: 3.3835
|
||||
warmup_momentum: 0.59462
|
||||
warmup_bias_lr: 0.18657
|
||||
box: 0.02
|
||||
cls: 0.21638
|
||||
cls_pw: 0.5
|
||||
obj: 0.51728
|
||||
obj_pw: 0.67198
|
||||
iou_t: 0.2
|
||||
anchor_t: 3.3744
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.01041
|
||||
hsv_s: 0.54703
|
||||
hsv_v: 0.27739
|
||||
degrees: 0.0
|
||||
translate: 0.04591
|
||||
scale: 0.75544
|
||||
shear: 0.0
|
||||
perspective: 0.0
|
||||
flipud: 0.0
|
||||
fliplr: 0.5
|
||||
mosaic: 0.85834
|
||||
mixup: 0.04266
|
||||
copy_paste: 0.0
|
||||
anchors: 3.412
|
||||
36
third_party/yolov5/data/hyps/hyp.no-augmentation.yaml
vendored
Normal file
36
third_party/yolov5/data/hyps/hyp.no-augmentation.yaml
vendored
Normal file
@ -0,0 +1,36 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Hyperparameters when using Albumentations frameworks
|
||||
# python train.py --hyp hyp.no-augmentation.yaml
|
||||
# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.3 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 0.7 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
# this parameters are all zero since we want to use albumentation framework
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0 # image translation (+/- fraction)
|
||||
scale: 0 # image scale (+/- gain)
|
||||
shear: 0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.0 # image flip left-right (probability)
|
||||
mosaic: 0.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
35
third_party/yolov5/data/hyps/hyp.scratch-high.yaml
vendored
Normal file
35
third_party/yolov5/data/hyps/hyp.scratch-high.yaml
vendored
Normal file
@ -0,0 +1,35 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Hyperparameters for high-augmentation COCO training from scratch
|
||||
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.3 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 0.7 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.9 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.1 # image mixup (probability)
|
||||
copy_paste: 0.1 # segment copy-paste (probability)
|
||||
35
third_party/yolov5/data/hyps/hyp.scratch-low.yaml
vendored
Normal file
35
third_party/yolov5/data/hyps/hyp.scratch-low.yaml
vendored
Normal file
@ -0,0 +1,35 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Hyperparameters for low-augmentation COCO training from scratch
|
||||
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.5 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 1.0 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.5 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
35
third_party/yolov5/data/hyps/hyp.scratch-med.yaml
vendored
Normal file
35
third_party/yolov5/data/hyps/hyp.scratch-med.yaml
vendored
Normal file
@ -0,0 +1,35 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Hyperparameters for medium-augmentation COCO training from scratch
|
||||
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.3 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 0.7 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.9 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.1 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
BIN
third_party/yolov5/data/images/bus.jpg
vendored
Normal file
BIN
third_party/yolov5/data/images/bus.jpg
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 476 KiB |
BIN
third_party/yolov5/data/images/zidane.jpg
vendored
Normal file
BIN
third_party/yolov5/data/images/zidane.jpg
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 165 KiB |
23
third_party/yolov5/data/scripts/download_weights.sh
vendored
Normal file
23
third_party/yolov5/data/scripts/download_weights.sh
vendored
Normal file
@ -0,0 +1,23 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Download latest models from https://github.com/ultralytics/yolov5/releases
|
||||
# Example usage: bash data/scripts/download_weights.sh
|
||||
# parent
|
||||
# └── yolov5
|
||||
# ├── yolov5s.pt ← downloads here
|
||||
# ├── yolov5m.pt
|
||||
# └── ...
|
||||
|
||||
python - << EOF
|
||||
from utils.downloads import attempt_download
|
||||
|
||||
p5 = list('nsmlx') # P5 models
|
||||
p6 = [f'{x}6' for x in p5] # P6 models
|
||||
cls = [f'{x}-cls' for x in p5] # classification models
|
||||
seg = [f'{x}-seg' for x in p5] # classification models
|
||||
|
||||
for x in p5 + p6 + cls + seg:
|
||||
attempt_download(f'weights/yolov5{x}.pt')
|
||||
|
||||
EOF
|
||||
57
third_party/yolov5/data/scripts/get_coco.sh
vendored
Normal file
57
third_party/yolov5/data/scripts/get_coco.sh
vendored
Normal file
@ -0,0 +1,57 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Download COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: bash data/scripts/get_coco.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
|
||||
if [ "$#" -gt 0 ]; then
|
||||
for opt in "$@"; do
|
||||
case "${opt}" in
|
||||
--train) train=true ;;
|
||||
--val) val=true ;;
|
||||
--test) test=true ;;
|
||||
--segments) segments=true ;;
|
||||
esac
|
||||
done
|
||||
else
|
||||
train=true
|
||||
val=true
|
||||
test=false
|
||||
segments=false
|
||||
fi
|
||||
|
||||
# Download/unzip labels
|
||||
d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
if [ "$segments" == "true" ]; then
|
||||
f='coco2017labels-segments.zip' # 168 MB
|
||||
else
|
||||
f='coco2017labels.zip' # 46 MB
|
||||
fi
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
|
||||
# Download/unzip images
|
||||
d='../datasets/coco/images' # unzip directory
|
||||
url=http://images.cocodataset.org/zips/
|
||||
if [ "$train" == "true" ]; then
|
||||
f='train2017.zip' # 19G, 118k images
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
fi
|
||||
if [ "$val" == "true" ]; then
|
||||
f='val2017.zip' # 1G, 5k images
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
fi
|
||||
if [ "$test" == "true" ]; then
|
||||
f='test2017.zip' # 7G, 41k images (optional)
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
fi
|
||||
wait # finish background tasks
|
||||
18
third_party/yolov5/data/scripts/get_coco128.sh
vendored
Normal file
18
third_party/yolov5/data/scripts/get_coco128.sh
vendored
Normal file
@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
||||
# Example usage: bash data/scripts/get_coco128.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here
|
||||
|
||||
# Download/unzip images and labels
|
||||
d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
|
||||
wait # finish background tasks
|
||||
52
third_party/yolov5/data/scripts/get_imagenet.sh
vendored
Normal file
52
third_party/yolov5/data/scripts/get_imagenet.sh
vendored
Normal file
@ -0,0 +1,52 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -gt 0 ]; then
|
||||
for opt in "$@"; do
|
||||
case "${opt}" in
|
||||
--train) train=true ;;
|
||||
--val) val=true ;;
|
||||
esac
|
||||
done
|
||||
else
|
||||
train=true
|
||||
val=true
|
||||
fi
|
||||
|
||||
# Make dir
|
||||
d='../datasets/imagenet' # unzip directory
|
||||
mkdir -p $d && cd $d
|
||||
|
||||
# Download/unzip train
|
||||
if [ "$train" == "true" ]; then
|
||||
wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
|
||||
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
|
||||
tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
|
||||
find . -name "*.tar" | while read NAME; do
|
||||
mkdir -p "${NAME%.tar}"
|
||||
tar -xf "${NAME}" -C "${NAME%.tar}"
|
||||
rm -f "${NAME}"
|
||||
done
|
||||
cd ..
|
||||
fi
|
||||
|
||||
# Download/unzip val
|
||||
if [ "$val" == "true" ]; then
|
||||
wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
|
||||
mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
|
||||
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
|
||||
fi
|
||||
|
||||
# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
|
||||
# rm train/n04266014/n04266014_10835.JPEG
|
||||
|
||||
# TFRecords (optional)
|
||||
# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt
|
||||
30
third_party/yolov5/data/scripts/get_imagenet10.sh
vendored
Normal file
30
third_party/yolov5/data/scripts/get_imagenet10.sh
vendored
Normal file
@ -0,0 +1,30 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -gt 0 ]; then
|
||||
for opt in "$@"; do
|
||||
case "${opt}" in
|
||||
--train) train=true ;;
|
||||
--val) val=true ;;
|
||||
esac
|
||||
done
|
||||
else
|
||||
train=true
|
||||
val=true
|
||||
fi
|
||||
|
||||
# Make dir
|
||||
d='../datasets/imagenet10' # unzip directory
|
||||
mkdir -p $d && cd $d
|
||||
|
||||
# Download/unzip train
|
||||
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip
|
||||
unzip imagenet10.zip && rm imagenet10.zip
|
||||
30
third_party/yolov5/data/scripts/get_imagenet100.sh
vendored
Normal file
30
third_party/yolov5/data/scripts/get_imagenet100.sh
vendored
Normal file
@ -0,0 +1,30 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -gt 0 ]; then
|
||||
for opt in "$@"; do
|
||||
case "${opt}" in
|
||||
--train) train=true ;;
|
||||
--val) val=true ;;
|
||||
esac
|
||||
done
|
||||
else
|
||||
train=true
|
||||
val=true
|
||||
fi
|
||||
|
||||
# Make dir
|
||||
d='../datasets/imagenet100' # unzip directory
|
||||
mkdir -p $d && cd $d
|
||||
|
||||
# Download/unzip train
|
||||
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet100.zip
|
||||
unzip imagenet100.zip && rm imagenet100.zip
|
||||
30
third_party/yolov5/data/scripts/get_imagenet1000.sh
vendored
Normal file
30
third_party/yolov5/data/scripts/get_imagenet1000.sh
vendored
Normal file
@ -0,0 +1,30 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
|
||||
if [ "$#" -gt 0 ]; then
|
||||
for opt in "$@"; do
|
||||
case "${opt}" in
|
||||
--train) train=true ;;
|
||||
--val) val=true ;;
|
||||
esac
|
||||
done
|
||||
else
|
||||
train=true
|
||||
val=true
|
||||
fi
|
||||
|
||||
# Make dir
|
||||
d='../datasets/imagenet1000' # unzip directory
|
||||
mkdir -p $d && cd $d
|
||||
|
||||
# Download/unzip train
|
||||
wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet1000.zip
|
||||
unzip imagenet1000.zip && rm imagenet1000.zip
|
||||
152
third_party/yolov5/data/xView.yaml
vendored
Normal file
152
third_party/yolov5/data/xView.yaml
vendored
Normal file
@ -0,0 +1,152 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
|
||||
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
|
||||
# Example usage: python train.py --data xView.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── xView ← downloads here (20.7 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/xView # dataset root dir
|
||||
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
||||
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: Fixed-wing Aircraft
|
||||
1: Small Aircraft
|
||||
2: Cargo Plane
|
||||
3: Helicopter
|
||||
4: Passenger Vehicle
|
||||
5: Small Car
|
||||
6: Bus
|
||||
7: Pickup Truck
|
||||
8: Utility Truck
|
||||
9: Truck
|
||||
10: Cargo Truck
|
||||
11: Truck w/Box
|
||||
12: Truck Tractor
|
||||
13: Trailer
|
||||
14: Truck w/Flatbed
|
||||
15: Truck w/Liquid
|
||||
16: Crane Truck
|
||||
17: Railway Vehicle
|
||||
18: Passenger Car
|
||||
19: Cargo Car
|
||||
20: Flat Car
|
||||
21: Tank car
|
||||
22: Locomotive
|
||||
23: Maritime Vessel
|
||||
24: Motorboat
|
||||
25: Sailboat
|
||||
26: Tugboat
|
||||
27: Barge
|
||||
28: Fishing Vessel
|
||||
29: Ferry
|
||||
30: Yacht
|
||||
31: Container Ship
|
||||
32: Oil Tanker
|
||||
33: Engineering Vehicle
|
||||
34: Tower crane
|
||||
35: Container Crane
|
||||
36: Reach Stacker
|
||||
37: Straddle Carrier
|
||||
38: Mobile Crane
|
||||
39: Dump Truck
|
||||
40: Haul Truck
|
||||
41: Scraper/Tractor
|
||||
42: Front loader/Bulldozer
|
||||
43: Excavator
|
||||
44: Cement Mixer
|
||||
45: Ground Grader
|
||||
46: Hut/Tent
|
||||
47: Shed
|
||||
48: Building
|
||||
49: Aircraft Hangar
|
||||
50: Damaged Building
|
||||
51: Facility
|
||||
52: Construction Site
|
||||
53: Vehicle Lot
|
||||
54: Helipad
|
||||
55: Storage Tank
|
||||
56: Shipping container lot
|
||||
57: Shipping Container
|
||||
58: Pylon
|
||||
59: Tower
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.dataloaders import autosplit
|
||||
from utils.general import download, xyxy2xywhn
|
||||
|
||||
|
||||
def convert_labels(fname=Path('xView/xView_train.geojson')):
|
||||
# Convert xView geoJSON labels to YOLO format
|
||||
path = fname.parent
|
||||
with open(fname) as f:
|
||||
print(f'Loading {fname}...')
|
||||
data = json.load(f)
|
||||
|
||||
# Make dirs
|
||||
labels = Path(path / 'labels' / 'train')
|
||||
os.system(f'rm -rf {labels}')
|
||||
labels.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# xView classes 11-94 to 0-59
|
||||
xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
|
||||
12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
|
||||
29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
|
||||
47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
|
||||
|
||||
shapes = {}
|
||||
for feature in tqdm(data['features'], desc=f'Converting {fname}'):
|
||||
p = feature['properties']
|
||||
if p['bounds_imcoords']:
|
||||
id = p['image_id']
|
||||
file = path / 'train_images' / id
|
||||
if file.exists(): # 1395.tif missing
|
||||
try:
|
||||
box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
|
||||
assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
|
||||
cls = p['type_id']
|
||||
cls = xview_class2index[int(cls)] # xView class to 0-60
|
||||
assert 59 >= cls >= 0, f'incorrect class index {cls}'
|
||||
|
||||
# Write YOLO label
|
||||
if id not in shapes:
|
||||
shapes[id] = Image.open(file).size
|
||||
box = xyxy2xywhn(box[None].astype(np.float64), w=shapes[id][0], h=shapes[id][1], clip=True)
|
||||
with open((labels / id).with_suffix('.txt'), 'a') as f:
|
||||
f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
|
||||
except Exception as e:
|
||||
print(f'WARNING: skipping one label for {file}: {e}')
|
||||
|
||||
|
||||
# Download manually from https://challenge.xviewdataset.org
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
# urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
|
||||
# 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
|
||||
# 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
|
||||
# download(urls, dir=dir, delete=False)
|
||||
|
||||
# Convert labels
|
||||
convert_labels(dir / 'xView_train.geojson')
|
||||
|
||||
# Move images
|
||||
images = Path(dir / 'images')
|
||||
images.mkdir(parents=True, exist_ok=True)
|
||||
Path(dir / 'train_images').rename(dir / 'images' / 'train')
|
||||
Path(dir / 'val_images').rename(dir / 'images' / 'val')
|
||||
|
||||
# Split
|
||||
autosplit(dir / 'images' / 'train')
|
||||
436
third_party/yolov5/detect.py
vendored
Normal file
436
third_party/yolov5/detect.py
vendored
Normal file
@ -0,0 +1,436 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
||||
|
||||
Usage - sources:
|
||||
$ python detect.py --weights yolov5s.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/LNwODJXcvt4' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
|
||||
Usage - formats:
|
||||
$ python detect.py --weights yolov5s.pt # PyTorch
|
||||
yolov5s.torchscript # TorchScript
|
||||
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s_openvino_model # OpenVINO
|
||||
yolov5s.engine # TensorRT
|
||||
yolov5s.mlpackage # CoreML (macOS-only)
|
||||
yolov5s_saved_model # TensorFlow SavedModel
|
||||
yolov5s.pb # TensorFlow GraphDef
|
||||
yolov5s.tflite # TensorFlow Lite
|
||||
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from ultralytics.utils.plotting import Annotator, colors, save_one_box
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
Profile,
|
||||
check_file,
|
||||
check_img_size,
|
||||
check_imshow,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
cv2,
|
||||
increment_path,
|
||||
non_max_suppression,
|
||||
print_args,
|
||||
scale_boxes,
|
||||
strip_optimizer,
|
||||
xyxy2xywh,
|
||||
)
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
weights=ROOT / "yolov5s.pt", # model path or triton URL
|
||||
source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
imgsz=(640, 640), # inference size (height, width)
|
||||
conf_thres=0.25, # confidence threshold
|
||||
iou_thres=0.45, # NMS IOU threshold
|
||||
max_det=1000, # maximum detections per image
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
view_img=False, # show results
|
||||
save_txt=False, # save results to *.txt
|
||||
save_format=0, # save boxes coordinates in YOLO format or Pascal-VOC format (0 for YOLO and 1 for Pascal-VOC)
|
||||
save_csv=False, # save results in CSV format
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_crop=False, # save cropped prediction boxes
|
||||
nosave=False, # do not save images/videos
|
||||
classes=None, # filter by class: --class 0, or --class 0 2 3
|
||||
agnostic_nms=False, # class-agnostic NMS
|
||||
augment=False, # augmented inference
|
||||
visualize=False, # visualize features
|
||||
update=False, # update all models
|
||||
project=ROOT / "runs/detect", # save results to project/name
|
||||
name="exp", # save results to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
line_thickness=3, # bounding box thickness (pixels)
|
||||
hide_labels=False, # hide labels
|
||||
hide_conf=False, # hide confidences
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
vid_stride=1, # video frame-rate stride
|
||||
):
|
||||
"""Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc.
|
||||
|
||||
Args:
|
||||
weights (str | Path): Path to the model weights file or a Triton URL. Default is 'yolov5s.pt'.
|
||||
source (str | Path): Input source, which can be a file, directory, URL, glob pattern, screen capture, or webcam
|
||||
index. Default is 'data/images'.
|
||||
data (str | Path): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
|
||||
imgsz (tuple[int, int]): Inference image size as a tuple (height, width). Default is (640, 640).
|
||||
conf_thres (float): Confidence threshold for detections. Default is 0.25.
|
||||
iou_thres (float): Intersection Over Union (IOU) threshold for non-max suppression. Default is 0.45.
|
||||
max_det (int): Maximum number of detections per image. Default is 1000.
|
||||
device (str): CUDA device identifier (e.g., '0' or '0,1,2,3') or 'cpu'. Default is an empty string, which uses
|
||||
the best available device.
|
||||
view_img (bool): If True, display inference results using OpenCV. Default is False.
|
||||
save_txt (bool): If True, save results in a text file. Default is False.
|
||||
save_csv (bool): If True, save results in a CSV file. Default is False.
|
||||
save_conf (bool): If True, include confidence scores in the saved results. Default is False.
|
||||
save_crop (bool): If True, save cropped prediction boxes. Default is False.
|
||||
nosave (bool): If True, do not save inference images or videos. Default is False.
|
||||
classes (list[int]): List of class indices to filter detections by. Default is None.
|
||||
agnostic_nms (bool): If True, perform class-agnostic non-max suppression. Default is False.
|
||||
augment (bool): If True, use augmented inference. Default is False.
|
||||
visualize (bool): If True, visualize feature maps. Default is False.
|
||||
update (bool): If True, update all models' weights. Default is False.
|
||||
project (str | Path): Directory to save results. Default is 'runs/detect'.
|
||||
name (str): Name of the current experiment; used to create a subdirectory within 'project'. Default is 'exp'.
|
||||
exist_ok (bool): If True, existing directories with the same name are reused instead of being incremented.
|
||||
Default is False.
|
||||
line_thickness (int): Thickness of bounding box lines in pixels. Default is 3.
|
||||
hide_labels (bool): If True, do not display labels on bounding boxes. Default is False.
|
||||
hide_conf (bool): If True, do not display confidence scores on bounding boxes. Default is False.
|
||||
half (bool): If True, use FP16 half-precision inference. Default is False.
|
||||
dnn (bool): If True, use OpenCV DNN backend for ONNX inference. Default is False.
|
||||
vid_stride (int): Stride for processing video frames, to skip frames between processing. Default is 1.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
```python
|
||||
from ultralytics import run
|
||||
|
||||
# Run inference on an image
|
||||
run(source='data/images/example.jpg', weights='yolov5s.pt', device='0')
|
||||
|
||||
# Run inference on a video with specific confidence threshold
|
||||
run(source='data/videos/example.mp4', weights='yolov5s.pt', conf_thres=0.4, device='0')
|
||||
```
|
||||
"""
|
||||
source = str(source)
|
||||
save_img = not nosave and not source.endswith(".txt") # save inference images
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
|
||||
webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
|
||||
screenshot = source.lower().startswith("screen")
|
||||
if is_url and is_file:
|
||||
source = check_file(source) # download
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
device = select_device(device)
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
|
||||
# Dataloader
|
||||
bs = 1 # batch_size
|
||||
if webcam:
|
||||
view_img = check_imshow(warn=True)
|
||||
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||||
bs = len(dataset)
|
||||
elif screenshot:
|
||||
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||||
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||
|
||||
# Run inference
|
||||
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
|
||||
seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
with dt[0]:
|
||||
im = torch.from_numpy(im).to(model.device)
|
||||
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
if model.xml and im.shape[0] > 1:
|
||||
ims = torch.chunk(im, im.shape[0], 0)
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
if model.xml and im.shape[0] > 1:
|
||||
pred = None
|
||||
for image in ims:
|
||||
if pred is None:
|
||||
pred = model(image, augment=augment, visualize=visualize).unsqueeze(0)
|
||||
else:
|
||||
pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0)
|
||||
pred = [pred, None]
|
||||
else:
|
||||
pred = model(im, augment=augment, visualize=visualize)
|
||||
# NMS
|
||||
with dt[2]:
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
||||
|
||||
# Second-stage classifier (optional)
|
||||
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||||
|
||||
# Define the path for the CSV file
|
||||
csv_path = save_dir / "predictions.csv"
|
||||
|
||||
# Create or append to the CSV file
|
||||
def write_to_csv(image_name, prediction, confidence):
|
||||
"""Writes prediction data for an image to a CSV file, appending if the file exists."""
|
||||
data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
|
||||
file_exists = os.path.isfile(csv_path)
|
||||
with open(csv_path, mode="a", newline="") as f:
|
||||
writer = csv.DictWriter(f, fieldnames=data.keys())
|
||||
if not file_exists:
|
||||
writer.writeheader()
|
||||
writer.writerow(data)
|
||||
|
||||
# Process predictions
|
||||
for i, det in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if webcam: # batch_size >= 1
|
||||
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||
s += f"{i}: "
|
||||
else:
|
||||
p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
|
||||
s += "{:g}x{:g} ".format(*im.shape[2:]) # print string
|
||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
imc = im0.copy() if save_crop else im0 # for save_crop
|
||||
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||||
if len(det):
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
|
||||
|
||||
# Print results
|
||||
for c in det[:, 5].unique():
|
||||
n = (det[:, 5] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
|
||||
# Write results
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
c = int(cls) # integer class
|
||||
label = names[c] if hide_conf else f"{names[c]}"
|
||||
confidence = float(conf)
|
||||
confidence_str = f"{confidence:.2f}"
|
||||
|
||||
if save_csv:
|
||||
write_to_csv(p.name, label, confidence_str)
|
||||
|
||||
if save_txt: # Write to file
|
||||
if save_format == 0:
|
||||
coords = (
|
||||
(xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
|
||||
) # normalized xywh
|
||||
else:
|
||||
coords = (torch.tensor(xyxy).view(1, 4) / gn).view(-1).tolist() # xyxy
|
||||
line = (cls, *coords, conf) if save_conf else (cls, *coords) # label format
|
||||
with open(f"{txt_path}.txt", "a") as f:
|
||||
f.write(("%g " * len(line)).rstrip() % line + "\n")
|
||||
|
||||
if save_img or save_crop or view_img: # Add bbox to image
|
||||
c = int(cls) # integer class
|
||||
label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
|
||||
annotator.box_label(xyxy, label, color=colors(c, True))
|
||||
if save_crop:
|
||||
save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
|
||||
|
||||
# Stream results
|
||||
im0 = annotator.result()
|
||||
if view_img:
|
||||
if platform.system() == "Linux" and p not in windows:
|
||||
windows.append(p)
|
||||
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||||
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||||
cv2.imshow(str(p), im0)
|
||||
cv2.waitKey(1) # 1 millisecond
|
||||
|
||||
# Save results (image with detections)
|
||||
if save_img:
|
||||
if dataset.mode == "image":
|
||||
cv2.imwrite(save_path, im0)
|
||||
else: # 'video' or 'stream'
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms")
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
if update:
|
||||
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parse command-line arguments for YOLOv5 detection, allowing custom inference options and model configurations.
|
||||
|
||||
Args:
|
||||
--weights (str | list[str], optional): Model path or Triton URL. Defaults to ROOT / 'yolov5s.pt'.
|
||||
--source (str, optional): File/dir/URL/glob/screen/0(webcam). Defaults to ROOT / 'data/images'.
|
||||
--data (str, optional): Dataset YAML path. Provides dataset configuration information.
|
||||
--imgsz (list[int], optional): Inference size (height, width). Defaults to [640].
|
||||
--conf-thres (float, optional): Confidence threshold. Defaults to 0.25.
|
||||
--iou-thres (float, optional): NMS IoU threshold. Defaults to 0.45.
|
||||
--max-det (int, optional): Maximum number of detections per image. Defaults to 1000.
|
||||
--device (str, optional): CUDA device, i.e., '0' or '0,1,2,3' or 'cpu'. Defaults to "".
|
||||
--view-img (bool, optional): Flag to display results. Defaults to False.
|
||||
--save-txt (bool, optional): Flag to save results to *.txt files. Defaults to False.
|
||||
--save-csv (bool, optional): Flag to save results in CSV format. Defaults to False.
|
||||
--save-conf (bool, optional): Flag to save confidences in labels saved via --save-txt. Defaults to False.
|
||||
--save-crop (bool, optional): Flag to save cropped prediction boxes. Defaults to False.
|
||||
--nosave (bool, optional): Flag to prevent saving images/videos. Defaults to False.
|
||||
--classes (list[int], optional): List of classes to filter results by, e.g., '--classes 0 2 3'. Defaults to
|
||||
None.
|
||||
--agnostic-nms (bool, optional): Flag for class-agnostic NMS. Defaults to False.
|
||||
--augment (bool, optional): Flag for augmented inference. Defaults to False.
|
||||
--visualize (bool, optional): Flag for visualizing features. Defaults to False.
|
||||
--update (bool, optional): Flag to update all models in the model directory. Defaults to False.
|
||||
--project (str, optional): Directory to save results. Defaults to ROOT / 'runs/detect'.
|
||||
--name (str, optional): Sub-directory name for saving results within --project. Defaults to 'exp'.
|
||||
--exist-ok (bool, optional): Flag to allow overwriting if the project/name already exists. Defaults to False.
|
||||
--line-thickness (int, optional): Thickness (in pixels) of bounding boxes. Defaults to 3.
|
||||
--hide-labels (bool, optional): Flag to hide labels in the output. Defaults to False.
|
||||
--hide-conf (bool, optional): Flag to hide confidences in the output. Defaults to False.
|
||||
--half (bool, optional): Flag to use FP16 half-precision inference. Defaults to False.
|
||||
--dnn (bool, optional): Flag to use OpenCV DNN for ONNX inference. Defaults to False.
|
||||
--vid-stride (int, optional): Video frame-rate stride, determining the number of frames to skip in between
|
||||
consecutive frames. Defaults to 1.
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: Parsed command-line arguments as an argparse.Namespace object.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
from ultralytics import YOLOv5
|
||||
args = YOLOv5.parse_opt()
|
||||
```
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL")
|
||||
parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
|
||||
parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")
|
||||
parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")
|
||||
parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--view-img", action="store_true", help="show results")
|
||||
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||||
parser.add_argument(
|
||||
"--save-format",
|
||||
type=int,
|
||||
default=0,
|
||||
help="whether to save boxes coordinates in YOLO format or Pascal-VOC format when save-txt is True, 0 for YOLO and 1 for Pascal-VOC",
|
||||
)
|
||||
parser.add_argument("--save-csv", action="store_true", help="save results in CSV format")
|
||||
parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
|
||||
parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
|
||||
parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
|
||||
parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3")
|
||||
parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS")
|
||||
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||||
parser.add_argument("--visualize", action="store_true", help="visualize features")
|
||||
parser.add_argument("--update", action="store_true", help="update all models")
|
||||
parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save results to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
|
||||
parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
|
||||
parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running.
|
||||
|
||||
Args:
|
||||
opt (argparse.Namespace): Command-line arguments for YOLOv5 detection. See function `parse_opt` for details.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Notes:
|
||||
This function performs essential pre-execution checks and initiates the YOLOv5 detection process based on user-specified
|
||||
options. Refer to the usage guide and examples for more information about different sources and formats at:
|
||||
https://github.com/ultralytics/ultralytics
|
||||
|
||||
Example usage:
|
||||
|
||||
```python
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
```
|
||||
"""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
1525
third_party/yolov5/export.py
vendored
Normal file
1525
third_party/yolov5/export.py
vendored
Normal file
File diff suppressed because it is too large
Load Diff
502
third_party/yolov5/hubconf.py
vendored
Normal file
502
third_party/yolov5/hubconf.py
vendored
Normal file
@ -0,0 +1,502 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.
|
||||
|
||||
Usage:
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model
|
||||
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model
|
||||
model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo
|
||||
"""
|
||||
|
||||
from ultralytics.utils.patches import torch_load
|
||||
|
||||
|
||||
def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
"""Creates or loads a YOLOv5 model, with options for pretrained weights and model customization.
|
||||
|
||||
Args:
|
||||
name (str): Model name (e.g., 'yolov5s') or path to the model checkpoint (e.g., 'path/to/best.pt').
|
||||
pretrained (bool, optional): If True, loads pretrained weights into the model. Defaults to True.
|
||||
channels (int, optional): Number of input channels the model expects. Defaults to 3.
|
||||
classes (int, optional): Number of classes the model is expected to detect. Defaults to 80.
|
||||
autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper for various input formats. Defaults
|
||||
to True.
|
||||
verbose (bool, optional): If True, prints detailed information during the model creation/loading process.
|
||||
Defaults to True.
|
||||
device (str | torch.device | None, optional): Device to use for model parameters (e.g., 'cpu', 'cuda'). If None,
|
||||
selects the best available device. Defaults to None.
|
||||
|
||||
Returns:
|
||||
(DetectMultiBackend | AutoShape): The loaded YOLOv5 model, potentially wrapped with AutoShape if specified.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
from ultralytics import _create
|
||||
|
||||
# Load an official YOLOv5s model with pretrained weights
|
||||
model = _create('yolov5s')
|
||||
|
||||
# Load a custom model from a local checkpoint
|
||||
model = _create('path/to/custom_model.pt', pretrained=False)
|
||||
|
||||
# Load a model with specific input channels and classes
|
||||
model = _create('yolov5s', channels=1, classes=10)
|
||||
```
|
||||
|
||||
Notes:
|
||||
For more information on model loading and customization, visit the
|
||||
[YOLOv5 PyTorch Hub Documentation](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/).
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
from models.common import AutoShape, DetectMultiBackend
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
|
||||
from utils.downloads import attempt_download
|
||||
from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
if not verbose:
|
||||
LOGGER.setLevel(logging.WARNING)
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop"))
|
||||
name = Path(name)
|
||||
path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path
|
||||
try:
|
||||
device = select_device(device)
|
||||
if pretrained and channels == 3 and classes == 80:
|
||||
try:
|
||||
model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
|
||||
if autoshape:
|
||||
if model.pt and isinstance(model.model, ClassificationModel):
|
||||
LOGGER.warning(
|
||||
"WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. "
|
||||
"You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)."
|
||||
)
|
||||
elif model.pt and isinstance(model.model, SegmentationModel):
|
||||
LOGGER.warning(
|
||||
"WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. "
|
||||
"You will not be able to run inference with this model."
|
||||
)
|
||||
else:
|
||||
model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
|
||||
except Exception:
|
||||
model = attempt_load(path, device=device, fuse=False) # arbitrary model
|
||||
else:
|
||||
cfg = next(iter((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))) # model.yaml path
|
||||
model = DetectionModel(cfg, channels, classes) # create model
|
||||
if pretrained:
|
||||
ckpt = torch_load(attempt_download(path), map_location=device) # load
|
||||
csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
if len(ckpt["model"].names) == classes:
|
||||
model.names = ckpt["model"].names # set class names attribute
|
||||
if not verbose:
|
||||
LOGGER.setLevel(logging.INFO) # reset to default
|
||||
return model.to(device)
|
||||
|
||||
except Exception as e:
|
||||
help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading"
|
||||
s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help."
|
||||
raise Exception(s) from e
|
||||
|
||||
|
||||
def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None):
|
||||
"""Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification.
|
||||
|
||||
Args:
|
||||
path (str): Path to the custom model file (e.g., 'path/to/model.pt').
|
||||
autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model if True, enabling compatibility with various input
|
||||
types (default is True).
|
||||
_verbose (bool): If True, prints all informational messages to the screen; otherwise, operates silently (default
|
||||
is True).
|
||||
device (str | torch.device | None): Device to load the model on, e.g., 'cpu', 'cuda', torch.device('cuda:0'),
|
||||
etc. (default is None, which automatically selects the best available device).
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: A YOLOv5 model loaded with the specified parameters.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
# Load model from a given path with autoshape enabled on the best available device
|
||||
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
|
||||
|
||||
# Load model from a local path without autoshape on the CPU device
|
||||
model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local', autoshape=False, device='cpu')
|
||||
```
|
||||
|
||||
Notes:
|
||||
For more details on loading models from PyTorch Hub:
|
||||
https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading
|
||||
"""
|
||||
return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
|
||||
|
||||
|
||||
def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Instantiates the YOLOv5-nano model with options for pretraining, input channels, class count, autoshaping,
|
||||
verbosity, and device.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, loads pretrained weights into the model. Defaults to True.
|
||||
channels (int): Number of input channels for the model. Defaults to 3.
|
||||
classes (int): Number of classes for object detection. Defaults to 80.
|
||||
autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper to the model for various formats
|
||||
(file/URI/PIL/cv2/np) and non-maximum suppression (NMS) during inference. Defaults to True.
|
||||
_verbose (bool): If True, prints detailed information to the screen. Defaults to True.
|
||||
device (str | torch.device | None): Specifies the device to use for model computation. If None, uses the best
|
||||
device available (i.e., GPU if available, otherwise CPU). Defaults to None.
|
||||
|
||||
Returns:
|
||||
DetectionModel | ClassificationModel | SegmentationModel: The instantiated YOLOv5-nano model, potentially with
|
||||
pretrained weights and autoshaping applied.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
from ultralytics import yolov5n
|
||||
|
||||
# Load the YOLOv5-nano model with defaults
|
||||
model = yolov5n()
|
||||
|
||||
# Load the YOLOv5-nano model with a specific device
|
||||
model = yolov5n(device='cuda')
|
||||
```
|
||||
|
||||
Notes:
|
||||
For further details on loading models from PyTorch Hub, refer to [PyTorch Hub models](https://pytorch.org/hub/
|
||||
ultralytics_yolov5).
|
||||
"""
|
||||
return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Create a YOLOv5-small (yolov5s) model with options for pretraining, input channels, class count, autoshaping,
|
||||
verbosity, and device configuration.
|
||||
|
||||
Args:
|
||||
pretrained (bool, optional): Flag to load pretrained weights into the model. Defaults to True.
|
||||
channels (int, optional): Number of input channels. Defaults to 3.
|
||||
classes (int, optional): Number of model classes. Defaults to 80.
|
||||
autoshape (bool, optional): Whether to wrap the model with YOLOv5's .autoshape() for handling various input
|
||||
formats. Defaults to True.
|
||||
_verbose (bool, optional): Flag to print detailed information regarding model loading. Defaults to True.
|
||||
device (str | torch.device | None, optional): Device to use for model computation, can be 'cpu', 'cuda', or
|
||||
torch.device instances. If None, automatically selects the best available device. Defaults to None.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The YOLOv5-small model configured and loaded according to the specified parameters.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Load the official YOLOv5-small model with pretrained weights
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
|
||||
|
||||
# Load the YOLOv5-small model from a specific branch
|
||||
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s')
|
||||
|
||||
# Load a custom YOLOv5-small model from a local checkpoint
|
||||
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
|
||||
|
||||
# Load a local YOLOv5-small model specifying source as local repository
|
||||
model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')
|
||||
```
|
||||
|
||||
Notes:
|
||||
For more details on model loading and customization, visit
|
||||
the [YOLOv5 PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5/).
|
||||
"""
|
||||
return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping,
|
||||
verbosity, and device.
|
||||
|
||||
Args:
|
||||
pretrained (bool, optional): Whether to load pretrained weights into the model. Default is True.
|
||||
channels (int, optional): Number of input channels. Default is 3.
|
||||
classes (int, optional): Number of model classes. Default is 80.
|
||||
autoshape (bool, optional): Apply YOLOv5 .autoshape() wrapper to the model for handling various input formats.
|
||||
Default is True.
|
||||
_verbose (bool, optional): Whether to print detailed information to the screen. Default is True.
|
||||
device (str | torch.device | None, optional): Device specification to use for model parameters (e.g., 'cpu',
|
||||
'cuda'). Default is None.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The instantiated YOLOv5-medium model.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5m') # Load YOLOv5-medium from Ultralytics repository
|
||||
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5m') # Load from the master branch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m.pt') # Load a custom/local YOLOv5-medium model
|
||||
model = torch.hub.load('.', 'custom', 'yolov5m.pt', source='local') # Load from a local repository
|
||||
```
|
||||
|
||||
Notes:
|
||||
For more information, visit https://pytorch.org/hub/ultralytics_yolov5.
|
||||
"""
|
||||
return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device
|
||||
selection.
|
||||
|
||||
Args:
|
||||
pretrained (bool): Load pretrained weights into the model. Default is True.
|
||||
channels (int): Number of input channels. Default is 3.
|
||||
classes (int): Number of model classes. Default is 80.
|
||||
autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model. Default is True.
|
||||
_verbose (bool): Print all information to screen. Default is True.
|
||||
device (str | torch.device | None): Device to use for model parameters, e.g., 'cpu', 'cuda', or a torch.device
|
||||
instance. Default is None.
|
||||
|
||||
Returns:
|
||||
YOLOv5 model (torch.nn.Module): The YOLOv5-large model instantiated with specified configurations and possibly
|
||||
pretrained weights.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5l')
|
||||
```
|
||||
|
||||
Notes:
|
||||
For additional details, refer to the PyTorch Hub models documentation:
|
||||
https://pytorch.org/hub/ultralytics_yolov5
|
||||
"""
|
||||
return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Perform object detection using the YOLOv5-xlarge model with options for pretraining, input channels, class count,
|
||||
autoshaping, verbosity, and device specification.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, loads pretrained weights into the model. Defaults to True.
|
||||
channels (int): Number of input channels for the model. Defaults to 3.
|
||||
classes (int): Number of model classes for object detection. Defaults to 80.
|
||||
autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper for handling different input formats.
|
||||
Defaults to True.
|
||||
_verbose (bool): If True, prints detailed information during model loading. Defaults to True.
|
||||
device (str | torch.device | None): Device specification for computing the model, e.g., 'cpu', 'cuda:0',
|
||||
torch.device('cuda'). Defaults to None.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The YOLOv5-xlarge model loaded with the specified parameters, optionally with pretrained
|
||||
weights and autoshaping applied.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5x')
|
||||
```
|
||||
|
||||
For additional details, refer to the official YOLOv5 PyTorch Hub models documentation:
|
||||
https://pytorch.org/hub/ultralytics_yolov5
|
||||
"""
|
||||
return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and device.
|
||||
|
||||
Args:
|
||||
pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True.
|
||||
channels (int, optional): Number of input channels. Default is 3.
|
||||
classes (int, optional): Number of model classes. Default is 80.
|
||||
autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper to the model. Default is True.
|
||||
_verbose (bool, optional): If True, prints all information to screen. Default is True.
|
||||
device (str | torch.device | None, optional): Device to use for model parameters. Can be 'cpu', 'cuda', or None.
|
||||
Default is None.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: YOLOv5-nano-P6 model loaded with the specified configurations.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
model = yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device='cuda')
|
||||
```
|
||||
|
||||
Notes:
|
||||
For more information on PyTorch Hub models, visit: https://pytorch.org/hub/ultralytics_yolov5
|
||||
"""
|
||||
return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Instantiate the YOLOv5-small-P6 model with options for pretraining, input channels, number of classes,
|
||||
autoshaping, verbosity, and device selection.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, loads pretrained weights. Default is True.
|
||||
channels (int): Number of input channels. Default is 3.
|
||||
classes (int): Number of object detection classes. Default is 80.
|
||||
autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model, allowing for varied input formats.
|
||||
Default is True.
|
||||
_verbose (bool): If True, prints detailed information during model loading. Default is True.
|
||||
device (str | torch.device | None): Device specification for model parameters (e.g., 'cpu', 'cuda', or
|
||||
torch.device). Default is None, which selects an available device automatically.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The YOLOv5-small-P6 model instance.
|
||||
|
||||
Raises:
|
||||
Exception: If there is an error during model creation or loading, with a suggestion to visit the YOLOv5
|
||||
tutorials for help.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s6')
|
||||
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s6') # load from a specific branch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5s6.pt') # custom/local model
|
||||
model = torch.hub.load('.', 'custom', 'path/to/yolov5s6.pt', source='local') # local repo model
|
||||
```
|
||||
|
||||
Notes:
|
||||
- For more information, refer to the PyTorch Hub models documentation at https://pytorch.org/hub/ultralytics_yolov5
|
||||
"""
|
||||
return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Create YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity,
|
||||
and device.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, loads pretrained weights. Default is True.
|
||||
channels (int): Number of input channels. Default is 3.
|
||||
classes (int): Number of model classes. Default is 80.
|
||||
autoshape (bool): Apply YOLOv5 .autoshape() wrapper to the model for file/URI/PIL/cv2/np inputs and NMS. Default
|
||||
is True.
|
||||
_verbose (bool): If True, prints detailed information to the screen. Default is True.
|
||||
device (str | torch.device | None): Device to use for model parameters. Default is None, which uses the best
|
||||
available device.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The YOLOv5-medium-P6 model.
|
||||
Refer to the PyTorch Hub models documentation: https://pytorch.org/hub/ultralytics_yolov5 for
|
||||
additional details.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Load YOLOv5-medium-P6 model
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5m6')
|
||||
```
|
||||
|
||||
Notes:
|
||||
- The model can be loaded with pre-trained weights for better performance on specific tasks.
|
||||
- The autoshape feature simplifies input handling by allowing various popular data formats.
|
||||
"""
|
||||
return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Instantiate the YOLOv5-large-P6 model with options for pretraining, channel and class counts, autoshaping,
|
||||
verbosity, and device selection.
|
||||
|
||||
Args:
|
||||
pretrained (bool, optional): If True, load pretrained weights into the model. Default is True.
|
||||
channels (int, optional): Number of input channels. Default is 3.
|
||||
classes (int, optional): Number of model classes. Default is 80.
|
||||
autoshape (bool, optional): If True, apply YOLOv5 .autoshape() wrapper to the model for input flexibility.
|
||||
Default is True.
|
||||
_verbose (bool, optional): If True, print all information to the screen. Default is True.
|
||||
device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', 'cuda', or
|
||||
torch.device. If None, automatically selects the best available device. Default is None.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The instantiated YOLOv5-large-P6 model.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5l6') # official model
|
||||
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5l6') # from specific branch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5l6.pt') # custom/local model
|
||||
model = torch.hub.load('.', 'custom', 'path/to/yolov5l6.pt', source='local') # local repository
|
||||
```
|
||||
|
||||
Notes:
|
||||
Refer to [PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5/) for additional usage instructions.
|
||||
"""
|
||||
return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
"""Creates the YOLOv5-xlarge-P6 model with options for pretraining, number of input channels, class count,
|
||||
autoshaping, verbosity, and device selection.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, loads pretrained weights into the model. Default is True.
|
||||
channels (int): Number of input channels. Default is 3.
|
||||
classes (int): Number of model classes. Default is 80.
|
||||
autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model. Default is True.
|
||||
_verbose (bool): If True, prints all information to the screen. Default is True.
|
||||
device (str | torch.device | None): Device to use for model parameters, can be a string, torch.device object, or
|
||||
None for default device selection. Default is None.
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: The instantiated YOLOv5-xlarge-P6 model.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5x6') # load the YOLOv5-xlarge-P6 model
|
||||
```
|
||||
|
||||
Notes:
|
||||
For more information on YOLOv5 models, visit the official documentation:
|
||||
https://docs.ultralytics.com/yolov5
|
||||
"""
|
||||
return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from utils.general import cv2, print_args
|
||||
|
||||
# Argparser
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--model", type=str, default="yolov5s", help="model name")
|
||||
opt = parser.parse_args()
|
||||
print_args(vars(opt))
|
||||
|
||||
# Model
|
||||
model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
|
||||
# model = custom(path='path/to/model.pt') # custom
|
||||
|
||||
# Images
|
||||
imgs = [
|
||||
"data/images/zidane.jpg", # filename
|
||||
Path("data/images/zidane.jpg"), # Path
|
||||
"https://ultralytics.com/images/zidane.jpg", # URI
|
||||
cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV
|
||||
Image.open("data/images/bus.jpg"), # PIL
|
||||
np.zeros((320, 640, 3)),
|
||||
] # numpy
|
||||
|
||||
# Inference
|
||||
results = model(imgs, size=320) # batched inference
|
||||
|
||||
# Results
|
||||
results.print()
|
||||
results.save()
|
||||
1
third_party/yolov5/models/__init__.py
vendored
Normal file
1
third_party/yolov5/models/__init__.py
vendored
Normal file
@ -0,0 +1 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
1111
third_party/yolov5/models/common.py
vendored
Normal file
1111
third_party/yolov5/models/common.py
vendored
Normal file
File diff suppressed because it is too large
Load Diff
130
third_party/yolov5/models/experimental.py
vendored
Normal file
130
third_party/yolov5/models/experimental.py
vendored
Normal file
@ -0,0 +1,130 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""Experimental modules."""
|
||||
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from ultralytics.utils.patches import torch_load
|
||||
|
||||
from utils.downloads import attempt_download
|
||||
|
||||
|
||||
class Sum(nn.Module):
|
||||
"""Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070."""
|
||||
|
||||
def __init__(self, n, weight=False):
|
||||
"""Initializes a module to sum outputs of layers with number of inputs `n` and optional weighting, supporting 2+
|
||||
inputs.
|
||||
"""
|
||||
super().__init__()
|
||||
self.weight = weight # apply weights boolean
|
||||
self.iter = range(n - 1) # iter object
|
||||
if weight:
|
||||
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights
|
||||
|
||||
def forward(self, x):
|
||||
"""Processes input through a customizable weighted sum of `n` inputs, optionally applying learned weights."""
|
||||
y = x[0] # no weight
|
||||
if self.weight:
|
||||
w = torch.sigmoid(self.w) * 2
|
||||
for i in self.iter:
|
||||
y = y + x[i + 1] * w[i]
|
||||
else:
|
||||
for i in self.iter:
|
||||
y = y + x[i + 1]
|
||||
return y
|
||||
|
||||
|
||||
class MixConv2d(nn.Module):
|
||||
"""Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595."""
|
||||
|
||||
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
|
||||
"""Initializes MixConv2d with mixed depth-wise convolutional layers, taking input and output channels (c1, c2),
|
||||
kernel sizes (k), stride (s), and channel distribution strategy (equal_ch).
|
||||
"""
|
||||
super().__init__()
|
||||
n = len(k) # number of convolutions
|
||||
if equal_ch: # equal c_ per group
|
||||
i = torch.linspace(0, n - 1e-6, c2).floor() # c2 indices
|
||||
c_ = [(i == g).sum() for g in range(n)] # intermediate channels
|
||||
else: # equal weight.numel() per group
|
||||
b = [c2] + [0] * n
|
||||
a = np.eye(n + 1, n, k=-1)
|
||||
a -= np.roll(a, 1, axis=1)
|
||||
a *= np.array(k) ** 2
|
||||
a[0] = 1
|
||||
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
|
||||
|
||||
self.m = nn.ModuleList(
|
||||
[nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]
|
||||
)
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.SiLU()
|
||||
|
||||
def forward(self, x):
|
||||
"""Performs forward pass by applying SiLU activation on batch-normalized concatenated convolutional layer
|
||||
outputs.
|
||||
"""
|
||||
return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
|
||||
|
||||
|
||||
class Ensemble(nn.ModuleList):
|
||||
"""Ensemble of models."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initializes an ensemble of models to be used for aggregated predictions."""
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x, augment=False, profile=False, visualize=False):
|
||||
"""Performs forward pass aggregating outputs from an ensemble of models.."""
|
||||
y = [module(x, augment, profile, visualize)[0] for module in self]
|
||||
# y = torch.stack(y).max(0)[0] # max ensemble
|
||||
# y = torch.stack(y).mean(0) # mean ensemble
|
||||
y = torch.cat(y, 1) # nms ensemble
|
||||
return y, None # inference, train output
|
||||
|
||||
|
||||
def attempt_load(weights, device=None, inplace=True, fuse=True):
|
||||
"""Loads and fuses an ensemble or single YOLOv5 model from weights, handling device placement and model adjustments.
|
||||
|
||||
Example inputs: weights=[a,b,c] or a single model weights=[a] or weights=a.
|
||||
"""
|
||||
from models.yolo import Detect, Model
|
||||
|
||||
model = Ensemble()
|
||||
for w in weights if isinstance(weights, list) else [weights]:
|
||||
ckpt = torch_load(attempt_download(w), map_location="cpu") # load
|
||||
ckpt = (ckpt.get("ema") or ckpt["model"]).to(device).float() # FP32 model
|
||||
|
||||
# Model compatibility updates
|
||||
if not hasattr(ckpt, "stride"):
|
||||
ckpt.stride = torch.tensor([32.0])
|
||||
if hasattr(ckpt, "names") and isinstance(ckpt.names, (list, tuple)):
|
||||
ckpt.names = dict(enumerate(ckpt.names)) # convert to dict
|
||||
|
||||
model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, "fuse") else ckpt.eval()) # model in eval mode
|
||||
|
||||
# Module updates
|
||||
for m in model.modules():
|
||||
t = type(m)
|
||||
if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
|
||||
m.inplace = inplace
|
||||
if t is Detect and not isinstance(m.anchor_grid, list):
|
||||
delattr(m, "anchor_grid")
|
||||
setattr(m, "anchor_grid", [torch.zeros(1)] * m.nl)
|
||||
elif t is nn.Upsample and not hasattr(m, "recompute_scale_factor"):
|
||||
m.recompute_scale_factor = None # torch 1.11.0 compatibility
|
||||
|
||||
# Return model
|
||||
if len(model) == 1:
|
||||
return model[-1]
|
||||
|
||||
# Return detection ensemble
|
||||
print(f"Ensemble created with {weights}\n")
|
||||
for k in "names", "nc", "yaml":
|
||||
setattr(model, k, getattr(model[0], k))
|
||||
model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
|
||||
assert all(model[0].nc == m.nc for m in model), f"Models have different class counts: {[m.nc for m in model]}"
|
||||
return model
|
||||
57
third_party/yolov5/models/hub/anchors.yaml
vendored
Normal file
57
third_party/yolov5/models/hub/anchors.yaml
vendored
Normal file
@ -0,0 +1,57 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Default anchors for COCO data
|
||||
|
||||
# P5 -------------------------------------------------------------------------------------------------------------------
|
||||
# P5-640:
|
||||
anchors_p5_640:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# P6 -------------------------------------------------------------------------------------------------------------------
|
||||
# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387
|
||||
anchors_p6_640:
|
||||
- [9, 11, 21, 19, 17, 41] # P3/8
|
||||
- [43, 32, 39, 70, 86, 64] # P4/16
|
||||
- [65, 131, 134, 130, 120, 265] # P5/32
|
||||
- [282, 180, 247, 354, 512, 387] # P6/64
|
||||
|
||||
# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792
|
||||
anchors_p6_1280:
|
||||
- [19, 27, 44, 40, 38, 94] # P3/8
|
||||
- [96, 68, 86, 152, 180, 137] # P4/16
|
||||
- [140, 301, 303, 264, 238, 542] # P5/32
|
||||
- [436, 615, 739, 380, 925, 792] # P6/64
|
||||
|
||||
# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187
|
||||
anchors_p6_1920:
|
||||
- [28, 41, 67, 59, 57, 141] # P3/8
|
||||
- [144, 103, 129, 227, 270, 205] # P4/16
|
||||
- [209, 452, 455, 396, 358, 812] # P5/32
|
||||
- [653, 922, 1109, 570, 1387, 1187] # P6/64
|
||||
|
||||
# P7 -------------------------------------------------------------------------------------------------------------------
|
||||
# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372
|
||||
anchors_p7_640:
|
||||
- [11, 11, 13, 30, 29, 20] # P3/8
|
||||
- [30, 46, 61, 38, 39, 92] # P4/16
|
||||
- [78, 80, 146, 66, 79, 163] # P5/32
|
||||
- [149, 150, 321, 143, 157, 303] # P6/64
|
||||
- [257, 402, 359, 290, 524, 372] # P7/128
|
||||
|
||||
# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818
|
||||
anchors_p7_1280:
|
||||
- [19, 22, 54, 36, 32, 77] # P3/8
|
||||
- [70, 83, 138, 71, 75, 173] # P4/16
|
||||
- [165, 159, 148, 334, 375, 151] # P5/32
|
||||
- [334, 317, 251, 626, 499, 474] # P6/64
|
||||
- [750, 326, 534, 814, 1079, 818] # P7/128
|
||||
|
||||
# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227
|
||||
anchors_p7_1920:
|
||||
- [29, 34, 81, 55, 47, 115] # P3/8
|
||||
- [105, 124, 207, 107, 113, 259] # P4/16
|
||||
- [247, 238, 222, 500, 563, 227] # P5/32
|
||||
- [501, 476, 376, 939, 749, 711] # P6/64
|
||||
- [1126, 489, 801, 1222, 1618, 1227] # P7/128
|
||||
52
third_party/yolov5/models/hub/yolov3-spp.yaml
vendored
Normal file
52
third_party/yolov5/models/hub/yolov3-spp.yaml
vendored
Normal file
@ -0,0 +1,52 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
|
||||
# YOLOv3-SPP head
|
||||
head: [
|
||||
[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, SPP, [512, [5, 9, 13]]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
42
third_party/yolov5/models/hub/yolov3-tiny.yaml
vendored
Normal file
42
third_party/yolov5/models/hub/yolov3-tiny.yaml
vendored
Normal file
@ -0,0 +1,42 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 14, 23, 27, 37, 58] # P4/16
|
||||
- [81, 82, 135, 169, 344, 319] # P5/32
|
||||
|
||||
# YOLOv3-tiny backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [16, 3, 1]], # 0
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
|
||||
[-1, 1, Conv, [32, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
|
||||
[-1, 1, Conv, [64, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
|
||||
[-1, 1, Conv, [128, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
|
||||
[-1, 1, Conv, [256, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
|
||||
[-1, 1, Conv, [512, 3, 1]],
|
||||
[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
|
||||
[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
|
||||
]
|
||||
|
||||
# YOLOv3-tiny head
|
||||
head: [
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
|
||||
|
||||
[[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5)
|
||||
]
|
||||
52
third_party/yolov5/models/hub/yolov3.yaml
vendored
Normal file
52
third_party/yolov5/models/hub/yolov3.yaml
vendored
Normal file
@ -0,0 +1,52 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
|
||||
# YOLOv3 head
|
||||
head: [
|
||||
[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/hub/yolov5-bifpn.yaml
vendored
Normal file
49
third_party/yolov5/models/hub/yolov5-bifpn.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 BiFPN head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
43
third_party/yolov5/models/hub/yolov5-fpn.yaml
vendored
Normal file
43
third_party/yolov5/models/hub/yolov5-fpn.yaml
vendored
Normal file
@ -0,0 +1,43 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 FPN head
|
||||
head: [
|
||||
[-1, 3, C3, [1024, False]], # 10 (P5/32-large)
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 3, C3, [512, False]], # 14 (P4/16-medium)
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 3, C3, [256, False]], # 18 (P3/8-small)
|
||||
|
||||
[[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
55
third_party/yolov5/models/hub/yolov5-p2.yaml
vendored
Normal file
55
third_party/yolov5/models/hub/yolov5-p2.yaml
vendored
Normal file
@ -0,0 +1,55 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 2], 1, Concat, [1]], # cat backbone P2
|
||||
[-1, 1, C3, [128, False]], # 21 (P2/4-xsmall)
|
||||
|
||||
[-1, 1, Conv, [128, 3, 2]],
|
||||
[[-1, 18], 1, Concat, [1]], # cat head P3
|
||||
[-1, 3, C3, [256, False]], # 24 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 27 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 30 (P5/32-large)
|
||||
|
||||
[[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5)
|
||||
]
|
||||
42
third_party/yolov5/models/hub/yolov5-p34.yaml
vendored
Normal file
42
third_party/yolov5/models/hub/yolov5-p34.yaml
vendored
Normal file
@ -0,0 +1,42 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head with (P3, P4) outputs
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[[17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4)
|
||||
]
|
||||
57
third_party/yolov5/models/hub/yolov5-p6.yaml
vendored
Normal file
57
third_party/yolov5/models/hub/yolov5-p6.yaml
vendored
Normal file
@ -0,0 +1,57 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs
|
||||
head: [
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
68
third_party/yolov5/models/hub/yolov5-p7.yaml
vendored
Normal file
68
third_party/yolov5/models/hub/yolov5-p7.yaml
vendored
Normal file
@ -0,0 +1,68 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
|
||||
[-1, 3, C3, [1280]],
|
||||
[-1, 1, SPPF, [1280, 5]], # 13
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs
|
||||
head: [
|
||||
[-1, 1, Conv, [1024, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat backbone P6
|
||||
[-1, 3, C3, [1024, False]], # 17
|
||||
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 21
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 25
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 29 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 26], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 32 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 22], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 35 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 18], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge)
|
||||
|
||||
[-1, 1, Conv, [1024, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P7
|
||||
[-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge)
|
||||
|
||||
[[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7)
|
||||
]
|
||||
49
third_party/yolov5/models/hub/yolov5-panet.yaml
vendored
Normal file
49
third_party/yolov5/models/hub/yolov5-panet.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 PANet head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
61
third_party/yolov5/models/hub/yolov5l6.yaml
vendored
Normal file
61
third_party/yolov5/models/hub/yolov5l6.yaml
vendored
Normal file
@ -0,0 +1,61 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [19, 27, 44, 40, 38, 94] # P3/8
|
||||
- [96, 68, 86, 152, 180, 137] # P4/16
|
||||
- [140, 301, 303, 264, 238, 542] # P5/32
|
||||
- [436, 615, 739, 380, 925, 792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
61
third_party/yolov5/models/hub/yolov5m6.yaml
vendored
Normal file
61
third_party/yolov5/models/hub/yolov5m6.yaml
vendored
Normal file
@ -0,0 +1,61 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
anchors:
|
||||
- [19, 27, 44, 40, 38, 94] # P3/8
|
||||
- [96, 68, 86, 152, 180, 137] # P4/16
|
||||
- [140, 301, 303, 264, 238, 542] # P5/32
|
||||
- [436, 615, 739, 380, 925, 792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
61
third_party/yolov5/models/hub/yolov5n6.yaml
vendored
Normal file
61
third_party/yolov5/models/hub/yolov5n6.yaml
vendored
Normal file
@ -0,0 +1,61 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.25 # layer channel multiple
|
||||
anchors:
|
||||
- [19, 27, 44, 40, 38, 94] # P3/8
|
||||
- [96, 68, 86, 152, 180, 137] # P4/16
|
||||
- [140, 301, 303, 264, 238, 542] # P5/32
|
||||
- [436, 615, 739, 380, 925, 792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
50
third_party/yolov5/models/hub/yolov5s-LeakyReLU.yaml
vendored
Normal file
50
third_party/yolov5/models/hub/yolov5s-LeakyReLU.yaml
vendored
Normal file
@ -0,0 +1,50 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/hub/yolov5s-ghost.yaml
vendored
Normal file
49
third_party/yolov5/models/hub/yolov5s-ghost.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3Ghost, [128]],
|
||||
[-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3Ghost, [256]],
|
||||
[-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3Ghost, [512]],
|
||||
[-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3Ghost, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, GhostConv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3Ghost, [512, False]], # 13
|
||||
|
||||
[-1, 1, GhostConv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, GhostConv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, GhostConv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/hub/yolov5s-transformer.yaml
vendored
Normal file
49
third_party/yolov5/models/hub/yolov5s-transformer.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
61
third_party/yolov5/models/hub/yolov5s6.yaml
vendored
Normal file
61
third_party/yolov5/models/hub/yolov5s6.yaml
vendored
Normal file
@ -0,0 +1,61 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [19, 27, 44, 40, 38, 94] # P3/8
|
||||
- [96, 68, 86, 152, 180, 137] # P4/16
|
||||
- [140, 301, 303, 264, 238, 542] # P5/32
|
||||
- [436, 615, 739, 380, 925, 792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
61
third_party/yolov5/models/hub/yolov5x6.yaml
vendored
Normal file
61
third_party/yolov5/models/hub/yolov5x6.yaml
vendored
Normal file
@ -0,0 +1,61 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.33 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
anchors:
|
||||
- [19, 27, 44, 40, 38, 94] # P3/8
|
||||
- [96, 68, 86, 152, 180, 137] # P4/16
|
||||
- [140, 301, 303, 264, 238, 542] # P5/32
|
||||
- [436, 615, 739, 380, 925, 792] # P6/64
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [768]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [768, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P5
|
||||
[-1, 3, C3, [768, False]], # 15
|
||||
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 19
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 23 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 20], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 16], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [768, False]], # 29 (P5/32-large)
|
||||
|
||||
[-1, 1, Conv, [768, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
49
third_party/yolov5/models/segment/yolov5l-seg.yaml
vendored
Normal file
49
third_party/yolov5/models/segment/yolov5l-seg.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/segment/yolov5m-seg.yaml
vendored
Normal file
49
third_party/yolov5/models/segment/yolov5m-seg.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/segment/yolov5n-seg.yaml
vendored
Normal file
49
third_party/yolov5/models/segment/yolov5n-seg.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.25 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/segment/yolov5s-seg.yaml
vendored
Normal file
49
third_party/yolov5/models/segment/yolov5s-seg.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.5 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/segment/yolov5x-seg.yaml
vendored
Normal file
49
third_party/yolov5/models/segment/yolov5x-seg.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.33 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)
|
||||
]
|
||||
775
third_party/yolov5/models/tf.py
vendored
Normal file
775
third_party/yolov5/models/tf.py
vendored
Normal file
@ -0,0 +1,775 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
TensorFlow, Keras and TFLite versions of YOLOv5
|
||||
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127.
|
||||
|
||||
Usage:
|
||||
$ python models/tf.py --weights yolov5s.pt
|
||||
|
||||
Export:
|
||||
$ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# ROOT = ROOT.relative_to(Path.cwd()) # relative
|
||||
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from tensorflow import keras
|
||||
|
||||
from models.common import (
|
||||
C3,
|
||||
SPP,
|
||||
SPPF,
|
||||
Bottleneck,
|
||||
BottleneckCSP,
|
||||
C3x,
|
||||
Concat,
|
||||
Conv,
|
||||
CrossConv,
|
||||
DWConv,
|
||||
DWConvTranspose2d,
|
||||
Focus,
|
||||
autopad,
|
||||
)
|
||||
from models.experimental import MixConv2d, attempt_load
|
||||
from models.yolo import Detect, Segment
|
||||
from utils.activations import SiLU
|
||||
from utils.general import LOGGER, make_divisible, print_args
|
||||
|
||||
|
||||
class TFBN(keras.layers.Layer):
|
||||
"""TensorFlow BatchNormalization wrapper for initializing with optional pretrained weights."""
|
||||
|
||||
def __init__(self, w=None):
|
||||
"""Initializes a TensorFlow BatchNormalization layer with optional pretrained weights."""
|
||||
super().__init__()
|
||||
self.bn = keras.layers.BatchNormalization(
|
||||
beta_initializer=keras.initializers.Constant(w.bias.numpy()),
|
||||
gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
|
||||
moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),
|
||||
moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),
|
||||
epsilon=w.eps,
|
||||
)
|
||||
|
||||
def call(self, inputs):
|
||||
"""Applies batch normalization to the inputs."""
|
||||
return self.bn(inputs)
|
||||
|
||||
|
||||
class TFPad(keras.layers.Layer):
|
||||
"""Pads input tensors in spatial dimensions 1 and 2 with specified integer or tuple padding values."""
|
||||
|
||||
def __init__(self, pad):
|
||||
"""Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and
|
||||
tuple inputs.
|
||||
|
||||
Inputs are
|
||||
"""
|
||||
super().__init__()
|
||||
if isinstance(pad, int):
|
||||
self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
|
||||
else: # tuple/list
|
||||
self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]])
|
||||
|
||||
def call(self, inputs):
|
||||
"""Pads input tensor with zeros using specified padding, suitable for int and tuple pad dimensions."""
|
||||
return tf.pad(inputs, self.pad, mode="constant", constant_values=0)
|
||||
|
||||
|
||||
class TFConv(keras.layers.Layer):
|
||||
"""Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow."""
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
|
||||
"""Initializes a standard convolution layer with optional batch normalization and activation; supports only
|
||||
group=1.
|
||||
|
||||
Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups.
|
||||
"""
|
||||
super().__init__()
|
||||
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
|
||||
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
|
||||
# see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
|
||||
conv = keras.layers.Conv2D(
|
||||
filters=c2,
|
||||
kernel_size=k,
|
||||
strides=s,
|
||||
padding="SAME" if s == 1 else "VALID",
|
||||
use_bias=not hasattr(w, "bn"),
|
||||
kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()),
|
||||
)
|
||||
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
|
||||
self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity
|
||||
self.act = activations(w.act) if act else tf.identity
|
||||
|
||||
def call(self, inputs):
|
||||
"""Applies convolution, batch normalization, and activation function to input tensors."""
|
||||
return self.act(self.bn(self.conv(inputs)))
|
||||
|
||||
|
||||
class TFDWConv(keras.layers.Layer):
|
||||
"""Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow."""
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
|
||||
"""Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow
|
||||
models.
|
||||
|
||||
Input are ch_in, ch_out, weights, kernel, stride, padding, groups.
|
||||
"""
|
||||
super().__init__()
|
||||
assert c2 % c1 == 0, f"TFDWConv() output={c2} must be a multiple of input={c1} channels"
|
||||
conv = keras.layers.DepthwiseConv2D(
|
||||
kernel_size=k,
|
||||
depth_multiplier=c2 // c1,
|
||||
strides=s,
|
||||
padding="SAME" if s == 1 else "VALID",
|
||||
use_bias=not hasattr(w, "bn"),
|
||||
depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()),
|
||||
)
|
||||
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
|
||||
self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity
|
||||
self.act = activations(w.act) if act else tf.identity
|
||||
|
||||
def call(self, inputs):
|
||||
"""Applies convolution, batch normalization, and activation function to input tensors."""
|
||||
return self.act(self.bn(self.conv(inputs)))
|
||||
|
||||
|
||||
class TFDWConvTranspose2d(keras.layers.Layer):
|
||||
"""Implements a depthwise ConvTranspose2D layer for TensorFlow with specific settings."""
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
|
||||
"""Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings.
|
||||
|
||||
Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups.
|
||||
"""
|
||||
super().__init__()
|
||||
assert c1 == c2, f"TFDWConv() output={c2} must be equal to input={c1} channels"
|
||||
assert k == 4 and p1 == 1, "TFDWConv() only valid for k=4 and p1=1"
|
||||
weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy()
|
||||
self.c1 = c1
|
||||
self.conv = [
|
||||
keras.layers.Conv2DTranspose(
|
||||
filters=1,
|
||||
kernel_size=k,
|
||||
strides=s,
|
||||
padding="VALID",
|
||||
output_padding=p2,
|
||||
use_bias=True,
|
||||
kernel_initializer=keras.initializers.Constant(weight[..., i : i + 1]),
|
||||
bias_initializer=keras.initializers.Constant(bias[i]),
|
||||
)
|
||||
for i in range(c1)
|
||||
]
|
||||
|
||||
def call(self, inputs):
|
||||
"""Processes input through parallel convolutions and concatenates results, trimming border pixels."""
|
||||
return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1]
|
||||
|
||||
|
||||
class TFFocus(keras.layers.Layer):
|
||||
"""Focuses spatial information into channel space using pixel shuffling and convolution for TensorFlow models."""
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
|
||||
"""Initializes TFFocus layer to focus width and height information into channel space with custom convolution
|
||||
parameters.
|
||||
|
||||
Inputs are ch_in, ch_out, kernel, stride, padding, groups.
|
||||
"""
|
||||
super().__init__()
|
||||
self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
|
||||
|
||||
def call(self, inputs):
|
||||
"""Performs pixel shuffling and convolution on input tensor, downsampling by 2 and expanding channels by 4.
|
||||
|
||||
Example x(b,w,h,c) -> y(b,w/2,h/2,4c).
|
||||
"""
|
||||
inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]]
|
||||
return self.conv(tf.concat(inputs, 3))
|
||||
|
||||
|
||||
class TFBottleneck(keras.layers.Layer):
|
||||
"""Implements a TensorFlow bottleneck layer with optional shortcut connections for efficient feature extraction."""
|
||||
|
||||
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with
|
||||
optional shortcut.
|
||||
|
||||
Arguments are ch_in, ch_out, shortcut, groups, expansion.
|
||||
"""
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def call(self, inputs):
|
||||
"""Performs forward pass; if shortcut is True & input/output channels match, adds input to the convolution
|
||||
result.
|
||||
"""
|
||||
return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
|
||||
|
||||
|
||||
class TFCrossConv(keras.layers.Layer):
|
||||
"""Implements a cross convolutional layer with optional expansion, grouping, and shortcut for TensorFlow."""
|
||||
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
|
||||
"""Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities."""
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1)
|
||||
self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def call(self, inputs):
|
||||
"""Passes input through two convolutions optionally adding the input if channel dimensions match."""
|
||||
return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
|
||||
|
||||
|
||||
class TFConv2d(keras.layers.Layer):
|
||||
"""Implements a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D for specified filters and stride."""
|
||||
|
||||
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
|
||||
"""Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter
|
||||
sizes and stride.
|
||||
"""
|
||||
super().__init__()
|
||||
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
|
||||
self.conv = keras.layers.Conv2D(
|
||||
filters=c2,
|
||||
kernel_size=k,
|
||||
strides=s,
|
||||
padding="VALID",
|
||||
use_bias=bias,
|
||||
kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None,
|
||||
)
|
||||
|
||||
def call(self, inputs):
|
||||
"""Applies a convolution operation to the inputs and returns the result."""
|
||||
return self.conv(inputs)
|
||||
|
||||
|
||||
class TFBottleneckCSP(keras.layers.Layer):
|
||||
"""Implements a CSP bottleneck layer for TensorFlow models to enhance gradient flow and efficiency."""
|
||||
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion
|
||||
ratio.
|
||||
|
||||
Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
|
||||
"""
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
|
||||
self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
|
||||
self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
|
||||
self.bn = TFBN(w.bn)
|
||||
self.act = lambda x: keras.activations.swish(x)
|
||||
self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
|
||||
|
||||
def call(self, inputs):
|
||||
"""Processes input through the model layers, concatenates, normalizes, activates, and reduces the output
|
||||
dimensions.
|
||||
"""
|
||||
y1 = self.cv3(self.m(self.cv1(inputs)))
|
||||
y2 = self.cv2(inputs)
|
||||
return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))
|
||||
|
||||
|
||||
class TFC3(keras.layers.Layer):
|
||||
"""CSP bottleneck layer with 3 convolutions for TensorFlow, supporting optional shortcuts and group convolutions."""
|
||||
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions.
|
||||
|
||||
Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
|
||||
"""
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
|
||||
self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
|
||||
self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
|
||||
|
||||
def call(self, inputs):
|
||||
"""Processes input through a sequence of transformations for object detection (YOLOv5).
|
||||
|
||||
See https://github.com/ultralytics/yolov5.
|
||||
"""
|
||||
return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
|
||||
|
||||
|
||||
class TFC3x(keras.layers.Layer):
|
||||
"""A TensorFlow layer for enhanced feature extraction using cross-convolutions in object detection models."""
|
||||
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""Initializes layer with cross-convolutions for enhanced feature extraction in object detection models.
|
||||
|
||||
Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
|
||||
"""
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
|
||||
self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
|
||||
self.m = keras.Sequential(
|
||||
[TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)]
|
||||
)
|
||||
|
||||
def call(self, inputs):
|
||||
"""Processes input through cascaded convolutions and merges features, returning the final tensor output."""
|
||||
return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
|
||||
|
||||
|
||||
class TFSPP(keras.layers.Layer):
|
||||
"""Implements spatial pyramid pooling for YOLOv3-SPP with specific channels and kernel sizes."""
|
||||
|
||||
def __init__(self, c1, c2, k=(5, 9, 13), w=None):
|
||||
"""Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling."""
|
||||
super().__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
|
||||
self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding="SAME") for x in k]
|
||||
|
||||
def call(self, inputs):
|
||||
"""Processes input through two TFConv layers and concatenates with max-pooled outputs at intermediate stage."""
|
||||
x = self.cv1(inputs)
|
||||
return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
|
||||
|
||||
|
||||
class TFSPPF(keras.layers.Layer):
|
||||
"""Implements a fast spatial pyramid pooling layer for TensorFlow with optimized feature extraction."""
|
||||
|
||||
def __init__(self, c1, c2, k=5, w=None):
|
||||
"""Initialize a fast spatial pyramid pooling layer with customizable channels, kernel size, and weights."""
|
||||
super().__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
|
||||
self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding="SAME")
|
||||
|
||||
def call(self, inputs):
|
||||
"""Executes the model's forward pass, concatenating input features with three max-pooled versions before final
|
||||
convolution.
|
||||
"""
|
||||
x = self.cv1(inputs)
|
||||
y1 = self.m(x)
|
||||
y2 = self.m(y1)
|
||||
return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
|
||||
|
||||
|
||||
class TFDetect(keras.layers.Layer):
|
||||
"""Implements YOLOv5 object detection layer in TensorFlow for predicting bounding boxes and class probabilities."""
|
||||
|
||||
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):
|
||||
"""Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image
|
||||
size.
|
||||
"""
|
||||
super().__init__()
|
||||
self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
|
||||
self.nc = nc # number of classes
|
||||
self.no = nc + 5 # number of outputs per anchor
|
||||
self.nl = len(anchors) # number of detection layers
|
||||
self.na = len(anchors[0]) // 2 # number of anchors
|
||||
self.grid = [tf.zeros(1)] * self.nl # init grid
|
||||
self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
|
||||
self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2])
|
||||
self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
|
||||
self.training = False # set to False after building model
|
||||
self.imgsz = imgsz
|
||||
for i in range(self.nl):
|
||||
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
|
||||
self.grid[i] = self._make_grid(nx, ny)
|
||||
|
||||
def call(self, inputs):
|
||||
"""Performs forward pass through the model layers to predict object bounding boxes and classifications."""
|
||||
z = [] # inference output
|
||||
x = []
|
||||
for i in range(self.nl):
|
||||
x.append(self.m[i](inputs[i]))
|
||||
# x(bs,20,20,255) to x(bs,3,20,20,85)
|
||||
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
|
||||
x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no])
|
||||
|
||||
if not self.training: # inference
|
||||
y = x[i]
|
||||
grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5
|
||||
anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4
|
||||
xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy
|
||||
wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid
|
||||
# Normalize xywh to 0-1 to reduce calibration error
|
||||
xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
|
||||
wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
|
||||
y = tf.concat([xy, wh, tf.sigmoid(y[..., 4 : 5 + self.nc]), y[..., 5 + self.nc :]], -1)
|
||||
z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
|
||||
|
||||
return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)
|
||||
|
||||
@staticmethod
|
||||
def _make_grid(nx=20, ny=20):
|
||||
"""Generates a 2D grid of coordinates in (x, y) format with shape [1, 1, ny*nx, 2]."""
|
||||
# return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
|
||||
xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))
|
||||
return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
|
||||
|
||||
|
||||
class TFSegment(TFDetect):
|
||||
"""YOLOv5 segmentation head for TensorFlow, combining detection and segmentation."""
|
||||
|
||||
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
|
||||
"""Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation
|
||||
models.
|
||||
"""
|
||||
super().__init__(nc, anchors, ch, imgsz, w)
|
||||
self.nm = nm # number of masks
|
||||
self.npr = npr # number of protos
|
||||
self.no = 5 + nc + self.nm # number of outputs per anchor
|
||||
self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv
|
||||
self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos
|
||||
self.detect = TFDetect.call
|
||||
|
||||
def call(self, x):
|
||||
"""Applies detection and proto layers on input, returning detections and optionally protos if training."""
|
||||
p = self.proto(x[0])
|
||||
# p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos
|
||||
p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160)
|
||||
x = self.detect(self, x)
|
||||
return (x, p) if self.training else (x[0], p)
|
||||
|
||||
|
||||
class TFProto(keras.layers.Layer):
|
||||
"""Implements convolutional and upsampling layers for feature extraction in YOLOv5 segmentation."""
|
||||
|
||||
def __init__(self, c1, c_=256, c2=32, w=None):
|
||||
"""Initialize TFProto layer with convolutional and upsampling for feature extraction and transformation."""
|
||||
super().__init__()
|
||||
self.cv1 = TFConv(c1, c_, k=3, w=w.cv1)
|
||||
self.upsample = TFUpsample(None, scale_factor=2, mode="nearest")
|
||||
self.cv2 = TFConv(c_, c_, k=3, w=w.cv2)
|
||||
self.cv3 = TFConv(c_, c2, w=w.cv3)
|
||||
|
||||
def call(self, inputs):
|
||||
"""Performs forward pass through the model, applying convolutions and upscaling on input tensor."""
|
||||
return self.cv3(self.cv2(self.upsample(self.cv1(inputs))))
|
||||
|
||||
|
||||
class TFUpsample(keras.layers.Layer):
|
||||
"""Implements a TensorFlow upsampling layer with specified size, scale factor, and interpolation mode."""
|
||||
|
||||
def __init__(self, size, scale_factor, mode, w=None):
|
||||
"""Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor
|
||||
is even.
|
||||
|
||||
Warning: all arguments needed including 'w'
|
||||
"""
|
||||
super().__init__()
|
||||
assert scale_factor % 2 == 0, "scale_factor must be multiple of 2"
|
||||
self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode)
|
||||
# self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
|
||||
# with default arguments: align_corners=False, half_pixel_centers=False
|
||||
# self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
|
||||
# size=(x.shape[1] * 2, x.shape[2] * 2))
|
||||
|
||||
def call(self, inputs):
|
||||
"""Applies upsample operation to inputs using nearest neighbor interpolation."""
|
||||
return self.upsample(inputs)
|
||||
|
||||
|
||||
class TFConcat(keras.layers.Layer):
|
||||
"""Implements TensorFlow's version of torch.concat() for concatenating tensors along the last dimension."""
|
||||
|
||||
def __init__(self, dimension=1, w=None):
|
||||
"""Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1."""
|
||||
super().__init__()
|
||||
assert dimension == 1, "convert only NCHW to NHWC concat"
|
||||
self.d = 3
|
||||
|
||||
def call(self, inputs):
|
||||
"""Concatenates a list of tensors along the last dimension, used for NCHW to NHWC conversion."""
|
||||
return tf.concat(inputs, self.d)
|
||||
|
||||
|
||||
def parse_model(d, ch, model, imgsz):
|
||||
"""Parses a model definition dict `d` to create YOLOv5 model layers, including dynamic channel adjustments."""
|
||||
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
|
||||
anchors, nc, gd, gw, ch_mul = (
|
||||
d["anchors"],
|
||||
d["nc"],
|
||||
d["depth_multiple"],
|
||||
d["width_multiple"],
|
||||
d.get("channel_multiple"),
|
||||
)
|
||||
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
|
||||
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
|
||||
if not ch_mul:
|
||||
ch_mul = 8
|
||||
|
||||
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
|
||||
for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
|
||||
m_str = m
|
||||
m = eval(m) if isinstance(m, str) else m # eval strings
|
||||
for j, a in enumerate(args):
|
||||
try:
|
||||
args[j] = eval(a) if isinstance(a, str) else a # eval strings
|
||||
except NameError:
|
||||
pass
|
||||
|
||||
n = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in [
|
||||
nn.Conv2d,
|
||||
Conv,
|
||||
DWConv,
|
||||
DWConvTranspose2d,
|
||||
Bottleneck,
|
||||
SPP,
|
||||
SPPF,
|
||||
MixConv2d,
|
||||
Focus,
|
||||
CrossConv,
|
||||
BottleneckCSP,
|
||||
C3,
|
||||
C3x,
|
||||
]:
|
||||
c1, c2 = ch[f], args[0]
|
||||
c2 = make_divisible(c2 * gw, ch_mul) if c2 != no else c2
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in [BottleneckCSP, C3, C3x]:
|
||||
args.insert(2, n)
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
|
||||
elif m in [Detect, Segment]:
|
||||
args.append([ch[x + 1] for x in f])
|
||||
if isinstance(args[1], int): # number of anchors
|
||||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||||
if m is Segment:
|
||||
args[3] = make_divisible(args[3] * gw, ch_mul)
|
||||
args.append(imgsz)
|
||||
else:
|
||||
c2 = ch[f]
|
||||
|
||||
tf_m = eval("TF" + m_str.replace("nn.", ""))
|
||||
m_ = (
|
||||
keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)])
|
||||
if n > 1
|
||||
else tf_m(*args, w=model.model[i])
|
||||
) # module
|
||||
|
||||
torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
|
||||
t = str(m)[8:-2].replace("__main__.", "") # module type
|
||||
np = sum(x.numel() for x in torch_m_.parameters()) # number params
|
||||
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
|
||||
LOGGER.info(f"{i:>3}{f!s:>18}{n!s:>3}{np:>10} {t:<40}{args!s:<30}") # print
|
||||
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
||||
layers.append(m_)
|
||||
ch.append(c2)
|
||||
return keras.Sequential(layers), sorted(save)
|
||||
|
||||
|
||||
class TFModel:
|
||||
"""Implements YOLOv5 model in TensorFlow, supporting TensorFlow, Keras, and TFLite formats for object detection."""
|
||||
|
||||
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)):
|
||||
"""Initialize TF YOLOv5 model with specified channels, classes, model instance, and input size."""
|
||||
super().__init__()
|
||||
if isinstance(cfg, dict):
|
||||
self.yaml = cfg # model dict
|
||||
else: # is *.yaml
|
||||
import yaml # for torch hub
|
||||
|
||||
self.yaml_file = Path(cfg).name
|
||||
with open(cfg) as f:
|
||||
self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
|
||||
|
||||
# Define model
|
||||
if nc and nc != self.yaml["nc"]:
|
||||
LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
|
||||
self.yaml["nc"] = nc # override yaml value
|
||||
self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
|
||||
|
||||
def predict(
|
||||
self,
|
||||
inputs,
|
||||
tf_nms=False,
|
||||
agnostic_nms=False,
|
||||
topk_per_class=100,
|
||||
topk_all=100,
|
||||
iou_thres=0.45,
|
||||
conf_thres=0.25,
|
||||
):
|
||||
"""Runs inference on input data, with an option for TensorFlow NMS."""
|
||||
y = [] # outputs
|
||||
x = inputs
|
||||
for m in self.model.layers:
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.savelist else None) # save output
|
||||
|
||||
# Add TensorFlow NMS
|
||||
if tf_nms:
|
||||
boxes = self._xywh2xyxy(x[0][..., :4])
|
||||
probs = x[0][:, :, 4:5]
|
||||
classes = x[0][:, :, 5:]
|
||||
scores = probs * classes
|
||||
if agnostic_nms:
|
||||
nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
|
||||
else:
|
||||
boxes = tf.expand_dims(boxes, 2)
|
||||
nms = tf.image.combined_non_max_suppression(
|
||||
boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False
|
||||
)
|
||||
return (nms,)
|
||||
return x # output [1,6300,85] = [xywh, conf, class0, class1, ...]
|
||||
# x = x[0] # [x(1,6300,85), ...] to x(6300,85)
|
||||
# xywh = x[..., :4] # x(6300,4) boxes
|
||||
# conf = x[..., 4:5] # x(6300,1) confidences
|
||||
# cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes
|
||||
# return tf.concat([conf, cls, xywh], 1)
|
||||
|
||||
@staticmethod
|
||||
def _xywh2xyxy(xywh):
|
||||
"""Convert box format from [x, y, w, h] to [x1, y1, x2, y2], where xy1=top-left and xy2=bottom- right."""
|
||||
x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
|
||||
return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)
|
||||
|
||||
|
||||
class AgnosticNMS(keras.layers.Layer):
|
||||
"""Performs agnostic non-maximum suppression (NMS) on detected objects using IoU and confidence thresholds."""
|
||||
|
||||
def call(self, input, topk_all, iou_thres, conf_thres):
|
||||
"""Performs agnostic NMS on input tensors using given thresholds and top-K selection."""
|
||||
return tf.map_fn(
|
||||
lambda x: self._nms(x, topk_all, iou_thres, conf_thres),
|
||||
input,
|
||||
fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
|
||||
name="agnostic_nms",
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):
|
||||
"""Performs agnostic non-maximum suppression (NMS) on detected objects, filtering based on IoU and confidence
|
||||
thresholds.
|
||||
"""
|
||||
boxes, classes, scores = x
|
||||
class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
|
||||
scores_inp = tf.reduce_max(scores, -1)
|
||||
selected_inds = tf.image.non_max_suppression(
|
||||
boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres
|
||||
)
|
||||
selected_boxes = tf.gather(boxes, selected_inds)
|
||||
padded_boxes = tf.pad(
|
||||
selected_boxes,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
|
||||
mode="CONSTANT",
|
||||
constant_values=0.0,
|
||||
)
|
||||
selected_scores = tf.gather(scores_inp, selected_inds)
|
||||
padded_scores = tf.pad(
|
||||
selected_scores,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
|
||||
mode="CONSTANT",
|
||||
constant_values=-1.0,
|
||||
)
|
||||
selected_classes = tf.gather(class_inds, selected_inds)
|
||||
padded_classes = tf.pad(
|
||||
selected_classes,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
|
||||
mode="CONSTANT",
|
||||
constant_values=-1.0,
|
||||
)
|
||||
valid_detections = tf.shape(selected_inds)[0]
|
||||
return padded_boxes, padded_scores, padded_classes, valid_detections
|
||||
|
||||
|
||||
def activations(act=nn.SiLU):
|
||||
"""Converts PyTorch activations to TensorFlow equivalents, supporting LeakyReLU, Hardswish, and SiLU/Swish."""
|
||||
if isinstance(act, nn.LeakyReLU):
|
||||
return lambda x: keras.activations.relu(x, alpha=0.1)
|
||||
elif isinstance(act, nn.Hardswish):
|
||||
return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667
|
||||
elif isinstance(act, (nn.SiLU, SiLU)):
|
||||
return lambda x: keras.activations.swish(x)
|
||||
else:
|
||||
raise Exception(f"no matching TensorFlow activation found for PyTorch activation {act}")
|
||||
|
||||
|
||||
def representative_dataset_gen(dataset, ncalib=100):
|
||||
"""Generate representative dataset for calibration by yielding transformed numpy arrays from the input dataset."""
|
||||
for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
|
||||
im = np.transpose(img, [1, 2, 0])
|
||||
im = np.expand_dims(im, axis=0).astype(np.float32)
|
||||
im /= 255
|
||||
yield [im]
|
||||
if n >= ncalib:
|
||||
break
|
||||
|
||||
|
||||
def run(
|
||||
weights=ROOT / "yolov5s.pt", # weights path
|
||||
imgsz=(640, 640), # inference size h,w
|
||||
batch_size=1, # batch size
|
||||
dynamic=False, # dynamic batch size
|
||||
):
|
||||
# PyTorch model
|
||||
"""Exports YOLOv5 model from PyTorch to TensorFlow and Keras formats, performing inference for validation."""
|
||||
im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image
|
||||
model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False)
|
||||
_ = model(im) # inference
|
||||
model.info()
|
||||
|
||||
# TensorFlow model
|
||||
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
_ = tf_model.predict(im) # inference
|
||||
|
||||
# Keras model
|
||||
im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
|
||||
keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
|
||||
keras_model.summary()
|
||||
|
||||
LOGGER.info("PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.")
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses and returns command-line options for model inference, including weights path, image size, batch size, and
|
||||
dynamic batching.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
|
||||
parser.add_argument("--batch-size", type=int, default=1, help="batch size")
|
||||
parser.add_argument("--dynamic", action="store_true", help="dynamic batch size")
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes the YOLOv5 model run function with parsed command line options."""
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
496
third_party/yolov5/models/yolo.py
vendored
Normal file
496
third_party/yolov5/models/yolo.py
vendored
Normal file
@ -0,0 +1,496 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
YOLO-specific modules.
|
||||
|
||||
Usage:
|
||||
$ python models/yolo.py --cfg yolov5s.yaml
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import contextlib
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
if platform.system() != "Windows":
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import (
|
||||
C3,
|
||||
C3SPP,
|
||||
C3TR,
|
||||
SPP,
|
||||
SPPF,
|
||||
Bottleneck,
|
||||
BottleneckCSP,
|
||||
C3Ghost,
|
||||
C3x,
|
||||
Classify,
|
||||
Concat,
|
||||
Contract,
|
||||
Conv,
|
||||
CrossConv,
|
||||
DetectMultiBackend,
|
||||
DWConv,
|
||||
DWConvTranspose2d,
|
||||
Expand,
|
||||
Focus,
|
||||
GhostBottleneck,
|
||||
GhostConv,
|
||||
Proto,
|
||||
)
|
||||
from models.experimental import MixConv2d
|
||||
from utils.autoanchor import check_anchor_order
|
||||
from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args
|
||||
from utils.plots import feature_visualization
|
||||
from utils.torch_utils import (
|
||||
fuse_conv_and_bn,
|
||||
initialize_weights,
|
||||
model_info,
|
||||
profile,
|
||||
scale_img,
|
||||
select_device,
|
||||
time_sync,
|
||||
)
|
||||
|
||||
try:
|
||||
import thop # for FLOPs computation
|
||||
except ImportError:
|
||||
thop = None
|
||||
|
||||
|
||||
class Detect(nn.Module):
|
||||
"""YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models."""
|
||||
|
||||
stride = None # strides computed during build
|
||||
dynamic = False # force grid reconstruction
|
||||
export = False # export mode
|
||||
|
||||
def __init__(self, nc=80, anchors=(), ch=(), inplace=True):
|
||||
"""Initializes YOLOv5 detection layer with specified classes, anchors, channels, and inplace operations."""
|
||||
super().__init__()
|
||||
self.nc = nc # number of classes
|
||||
self.no = nc + 5 # number of outputs per anchor
|
||||
self.nl = len(anchors) # number of detection layers
|
||||
self.na = len(anchors[0]) // 2 # number of anchors
|
||||
self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid
|
||||
self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid
|
||||
self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
|
||||
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
||||
self.inplace = inplace # use inplace ops (e.g. slice assignment)
|
||||
|
||||
def forward(self, x):
|
||||
"""Processes input through YOLOv5 layers, altering shape for detection: `x(bs, 3, ny, nx, 85)`."""
|
||||
z = [] # inference output
|
||||
for i in range(self.nl):
|
||||
x[i] = self.m[i](x[i]) # conv
|
||||
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
|
||||
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
|
||||
|
||||
if not self.training: # inference
|
||||
if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
|
||||
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
|
||||
|
||||
if isinstance(self, Segment): # (boxes + masks)
|
||||
xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
|
||||
xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
|
||||
else: # Detect (boxes only)
|
||||
xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
|
||||
xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
y = torch.cat((xy, wh, conf), 4)
|
||||
z.append(y.view(bs, self.na * nx * ny, self.no))
|
||||
|
||||
return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
|
||||
|
||||
def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")):
|
||||
"""Generates a mesh grid for anchor boxes with optional compatibility for torch versions < 1.10."""
|
||||
d = self.anchors[i].device
|
||||
t = self.anchors[i].dtype
|
||||
shape = 1, self.na, ny, nx, 2 # grid shape
|
||||
y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
|
||||
yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility
|
||||
grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5
|
||||
anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
|
||||
return grid, anchor_grid
|
||||
|
||||
|
||||
class Segment(Detect):
|
||||
"""YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers."""
|
||||
|
||||
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
|
||||
"""Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
|
||||
super().__init__(nc, anchors, ch, inplace)
|
||||
self.nm = nm # number of masks
|
||||
self.npr = npr # number of protos
|
||||
self.no = 5 + nc + self.nm # number of outputs per anchor
|
||||
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
||||
self.proto = Proto(ch[0], self.npr, self.nm) # protos
|
||||
self.detect = Detect.forward
|
||||
|
||||
def forward(self, x):
|
||||
"""Processes input through the network, returning detections and prototypes; adjusts output based on
|
||||
training/export mode.
|
||||
"""
|
||||
p = self.proto(x[0])
|
||||
x = self.detect(self, x)
|
||||
return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])
|
||||
|
||||
|
||||
class BaseModel(nn.Module):
|
||||
"""YOLOv5 base model."""
|
||||
|
||||
def forward(self, x, profile=False, visualize=False):
|
||||
"""Executes a single-scale inference or training pass on the YOLOv5 base model, with options for profiling and
|
||||
visualization.
|
||||
"""
|
||||
return self._forward_once(x, profile, visualize) # single-scale inference, train
|
||||
|
||||
def _forward_once(self, x, profile=False, visualize=False):
|
||||
"""Performs a forward pass on the YOLOv5 model, enabling profiling and feature visualization options."""
|
||||
y, dt = [], [] # outputs
|
||||
for m in self.model:
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
if profile:
|
||||
self._profile_one_layer(m, x, dt)
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.save else None) # save output
|
||||
if visualize:
|
||||
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
||||
return x
|
||||
|
||||
def _profile_one_layer(self, m, x, dt):
|
||||
"""Profiles a single layer's performance by computing GFLOPs, execution time, and parameters."""
|
||||
c = m == self.model[-1] # is final layer, copy input as inplace fix
|
||||
o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs
|
||||
t = time_sync()
|
||||
for _ in range(10):
|
||||
m(x.copy() if c else x)
|
||||
dt.append((time_sync() - t) * 100)
|
||||
if m == self.model[0]:
|
||||
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
|
||||
LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}")
|
||||
if c:
|
||||
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
||||
|
||||
def fuse(self):
|
||||
"""Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed."""
|
||||
LOGGER.info("Fusing layers... ")
|
||||
for m in self.model.modules():
|
||||
if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"):
|
||||
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
||||
delattr(m, "bn") # remove batchnorm
|
||||
m.forward = m.forward_fuse # update forward
|
||||
self.info()
|
||||
return self
|
||||
|
||||
def info(self, verbose=False, img_size=640):
|
||||
"""Prints model information given verbosity and image size, e.g., `info(verbose=True, img_size=640)`."""
|
||||
model_info(self, verbose, img_size)
|
||||
|
||||
def _apply(self, fn):
|
||||
"""Applies transformations like to(), cpu(), cuda(), half() to model tensors excluding parameters or registered
|
||||
buffers.
|
||||
"""
|
||||
self = super()._apply(fn)
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, (Detect, Segment)):
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
return self
|
||||
|
||||
|
||||
class DetectionModel(BaseModel):
|
||||
"""YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors."""
|
||||
|
||||
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
|
||||
"""Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
|
||||
super().__init__()
|
||||
if isinstance(cfg, dict):
|
||||
self.yaml = cfg # model dict
|
||||
else: # is *.yaml
|
||||
import yaml # for torch hub
|
||||
|
||||
self.yaml_file = Path(cfg).name
|
||||
with open(cfg, encoding="ascii", errors="ignore") as f:
|
||||
self.yaml = yaml.safe_load(f) # model dict
|
||||
|
||||
# Define model
|
||||
ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels
|
||||
if nc and nc != self.yaml["nc"]:
|
||||
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
|
||||
self.yaml["nc"] = nc # override yaml value
|
||||
if anchors:
|
||||
LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}")
|
||||
self.yaml["anchors"] = round(anchors) # override yaml value
|
||||
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
|
||||
self.names = [str(i) for i in range(self.yaml["nc"])] # default names
|
||||
self.inplace = self.yaml.get("inplace", True)
|
||||
|
||||
# Build strides, anchors
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, (Detect, Segment)):
|
||||
|
||||
def _forward(x):
|
||||
"""Passes the input 'x' through the model and returns the processed output."""
|
||||
return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
|
||||
|
||||
s = 256 # 2x min stride
|
||||
m.inplace = self.inplace
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
check_anchor_order(m)
|
||||
m.anchors /= m.stride.view(-1, 1, 1)
|
||||
self.stride = m.stride
|
||||
self._initialize_biases() # only run once
|
||||
|
||||
# Init weights, biases
|
||||
initialize_weights(self)
|
||||
self.info()
|
||||
LOGGER.info("")
|
||||
|
||||
def forward(self, x, augment=False, profile=False, visualize=False):
|
||||
"""Performs single-scale or augmented inference and may include profiling or visualization."""
|
||||
if augment:
|
||||
return self._forward_augment(x) # augmented inference, None
|
||||
return self._forward_once(x, profile, visualize) # single-scale inference, train
|
||||
|
||||
def _forward_augment(self, x):
|
||||
"""Performs augmented inference across different scales and flips, returning combined detections."""
|
||||
img_size = x.shape[-2:] # height, width
|
||||
s = [1, 0.83, 0.67] # scales
|
||||
f = [None, 3, None] # flips (2-ud, 3-lr)
|
||||
y = [] # outputs
|
||||
for si, fi in zip(s, f):
|
||||
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
|
||||
yi = self._forward_once(xi)[0] # forward
|
||||
# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
|
||||
yi = self._descale_pred(yi, fi, si, img_size)
|
||||
y.append(yi)
|
||||
y = self._clip_augmented(y) # clip augmented tails
|
||||
return torch.cat(y, 1), None # augmented inference, train
|
||||
|
||||
def _descale_pred(self, p, flips, scale, img_size):
|
||||
"""De-scales predictions from augmented inference, adjusting for flips and image size."""
|
||||
if self.inplace:
|
||||
p[..., :4] /= scale # de-scale
|
||||
if flips == 2:
|
||||
p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
|
||||
elif flips == 3:
|
||||
p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
|
||||
else:
|
||||
x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
|
||||
if flips == 2:
|
||||
y = img_size[0] - y # de-flip ud
|
||||
elif flips == 3:
|
||||
x = img_size[1] - x # de-flip lr
|
||||
p = torch.cat((x, y, wh, p[..., 4:]), -1)
|
||||
return p
|
||||
|
||||
def _clip_augmented(self, y):
|
||||
"""Clips augmented inference tails for YOLOv5 models, affecting first and last tensors based on grid points and
|
||||
layer counts.
|
||||
"""
|
||||
nl = self.model[-1].nl # number of detection layers (P3-P5)
|
||||
g = sum(4**x for x in range(nl)) # grid points
|
||||
e = 1 # exclude layer count
|
||||
i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices
|
||||
y[0] = y[0][:, :-i] # large
|
||||
i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
|
||||
y[-1] = y[-1][:, i:] # small
|
||||
return y
|
||||
|
||||
def _initialize_biases(self, cf=None):
|
||||
"""Initializes biases for YOLOv5's Detect() module, optionally using class frequencies (cf).
|
||||
|
||||
For details see https://arxiv.org/abs/1708.02002 section 3.3.
|
||||
"""
|
||||
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi, s in zip(m.m, m.stride): # from
|
||||
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
|
||||
b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
|
||||
b.data[:, 5 : 5 + m.nc] += (
|
||||
math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum())
|
||||
) # cls
|
||||
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
|
||||
|
||||
|
||||
Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility
|
||||
|
||||
|
||||
class SegmentationModel(DetectionModel):
|
||||
"""YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters."""
|
||||
|
||||
def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
|
||||
"""Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for
|
||||
channels, nc (int) for num classes, anchors (list).
|
||||
"""
|
||||
super().__init__(cfg, ch, nc, anchors)
|
||||
|
||||
|
||||
class ClassificationModel(BaseModel):
|
||||
"""YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""
|
||||
|
||||
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
|
||||
"""Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
|
||||
index.
|
||||
"""
|
||||
super().__init__()
|
||||
self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
|
||||
|
||||
def _from_detection_model(self, model, nc=1000, cutoff=10):
|
||||
"""Creates a classification model from a YOLOv5 detection model, slicing at `cutoff` and adding a classification
|
||||
layer.
|
||||
"""
|
||||
if isinstance(model, DetectMultiBackend):
|
||||
model = model.model # unwrap DetectMultiBackend
|
||||
model.model = model.model[:cutoff] # backbone
|
||||
m = model.model[-1] # last layer
|
||||
ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module
|
||||
c = Classify(ch, nc) # Classify()
|
||||
c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type
|
||||
model.model[-1] = c # replace
|
||||
self.model = model.model
|
||||
self.stride = model.stride
|
||||
self.save = []
|
||||
self.nc = nc
|
||||
|
||||
def _from_yaml(self, cfg):
|
||||
"""Creates a YOLOv5 classification model from a specified *.yaml configuration file."""
|
||||
self.model = None
|
||||
|
||||
|
||||
def parse_model(d, ch):
|
||||
"""Parses a YOLOv5 model from a dict `d`, configuring layers based on input channels `ch` and model architecture."""
|
||||
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
|
||||
anchors, nc, gd, gw, act, ch_mul = (
|
||||
d["anchors"],
|
||||
d["nc"],
|
||||
d["depth_multiple"],
|
||||
d["width_multiple"],
|
||||
d.get("activation"),
|
||||
d.get("channel_multiple"),
|
||||
)
|
||||
if act:
|
||||
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
|
||||
LOGGER.info(f"{colorstr('activation:')} {act}") # print
|
||||
if not ch_mul:
|
||||
ch_mul = 8
|
||||
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
|
||||
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
|
||||
|
||||
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
|
||||
for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
|
||||
m = eval(m) if isinstance(m, str) else m # eval strings
|
||||
for j, a in enumerate(args):
|
||||
with contextlib.suppress(NameError):
|
||||
args[j] = eval(a) if isinstance(a, str) else a # eval strings
|
||||
|
||||
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in {
|
||||
Conv,
|
||||
GhostConv,
|
||||
Bottleneck,
|
||||
GhostBottleneck,
|
||||
SPP,
|
||||
SPPF,
|
||||
DWConv,
|
||||
MixConv2d,
|
||||
Focus,
|
||||
CrossConv,
|
||||
BottleneckCSP,
|
||||
C3,
|
||||
C3TR,
|
||||
C3SPP,
|
||||
C3Ghost,
|
||||
nn.ConvTranspose2d,
|
||||
DWConvTranspose2d,
|
||||
C3x,
|
||||
}:
|
||||
c1, c2 = ch[f], args[0]
|
||||
if c2 != no: # if not output
|
||||
c2 = make_divisible(c2 * gw, ch_mul)
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
|
||||
args.insert(2, n) # number of repeats
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[x] for x in f)
|
||||
# TODO: channel, gw, gd
|
||||
elif m in {Detect, Segment}:
|
||||
args.append([ch[x] for x in f])
|
||||
if isinstance(args[1], int): # number of anchors
|
||||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||||
if m is Segment:
|
||||
args[3] = make_divisible(args[3] * gw, ch_mul)
|
||||
elif m is Contract:
|
||||
c2 = ch[f] * args[0] ** 2
|
||||
elif m is Expand:
|
||||
c2 = ch[f] // args[0] ** 2
|
||||
else:
|
||||
c2 = ch[f]
|
||||
|
||||
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
|
||||
t = str(m)[8:-2].replace("__main__.", "") # module type
|
||||
np = sum(x.numel() for x in m_.parameters()) # number params
|
||||
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
|
||||
LOGGER.info(f"{i:>3}{f!s:>18}{n_:>3}{np:10.0f} {t:<40}{args!s:<30}") # print
|
||||
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
||||
layers.append(m_)
|
||||
if i == 0:
|
||||
ch = []
|
||||
ch.append(c2)
|
||||
return nn.Sequential(*layers), sorted(save)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml")
|
||||
parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--profile", action="store_true", help="profile model speed")
|
||||
parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer")
|
||||
parser.add_argument("--test", action="store_true", help="test all yolo*.yaml")
|
||||
opt = parser.parse_args()
|
||||
opt.cfg = check_yaml(opt.cfg) # check YAML
|
||||
print_args(vars(opt))
|
||||
device = select_device(opt.device)
|
||||
|
||||
# Create model
|
||||
im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
|
||||
model = Model(opt.cfg).to(device)
|
||||
|
||||
# Options
|
||||
if opt.line_profile: # profile layer by layer
|
||||
model(im, profile=True)
|
||||
|
||||
elif opt.profile: # profile forward-backward
|
||||
results = profile(input=im, ops=[model], n=3)
|
||||
|
||||
elif opt.test: # test all models
|
||||
for cfg in Path(ROOT / "models").rglob("yolo*.yaml"):
|
||||
try:
|
||||
_ = Model(cfg)
|
||||
except Exception as e:
|
||||
print(f"Error in {cfg}: {e}")
|
||||
|
||||
else: # report fused model summary
|
||||
model.fuse()
|
||||
49
third_party/yolov5/models/yolov5l.yaml
vendored
Normal file
49
third_party/yolov5/models/yolov5l.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/yolov5m.yaml
vendored
Normal file
49
third_party/yolov5/models/yolov5m.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/yolov5n.yaml
vendored
Normal file
49
third_party/yolov5/models/yolov5n.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.25 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/yolov5s.yaml
vendored
Normal file
49
third_party/yolov5/models/yolov5s.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
49
third_party/yolov5/models/yolov5x.yaml
vendored
Normal file
49
third_party/yolov5/models/yolov5x.yaml
vendored
Normal file
@ -0,0 +1,49 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.33 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
anchors:
|
||||
- [10, 13, 16, 30, 33, 23] # P3/8
|
||||
- [30, 61, 62, 45, 59, 119] # P4/16
|
||||
- [116, 90, 156, 198, 373, 326] # P5/32
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[
|
||||
[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C3, [1024]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head: [
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
147
third_party/yolov5/pyproject.toml
vendored
Normal file
147
third_party/yolov5/pyproject.toml
vendored
Normal file
@ -0,0 +1,147 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Overview:
|
||||
# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library.
|
||||
# It defines essential project metadata, dependencies, and settings used to develop and deploy the library.
|
||||
|
||||
# Key Sections:
|
||||
# - [build-system]: Specifies the build requirements and backend (e.g., setuptools, wheel).
|
||||
# - [project]: Includes details like name, version, description, authors, dependencies and more.
|
||||
# - [project.optional-dependencies]: Provides additional, optional packages for extended features.
|
||||
# - [tool.*]: Configures settings for various tools (pytest, yapf, etc.) used in the project.
|
||||
|
||||
# Installation:
|
||||
# The Ultralytics library can be installed using the command: 'pip install ultralytics'
|
||||
# For development purposes, you can install the package in editable mode with: 'pip install -e .'
|
||||
# This approach allows for real-time code modifications without the need for re-installation.
|
||||
|
||||
# Documentation:
|
||||
# For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com
|
||||
|
||||
[build-system]
|
||||
requires = ["setuptools>=43.0.0", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
# Project settings -----------------------------------------------------------------------------------------------------
|
||||
[project]
|
||||
version = "7.0.0"
|
||||
name = "YOLOv5"
|
||||
description = "Ultralytics YOLOv5 for SOTA object detection, instance segmentation and image classification."
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.8"
|
||||
license = { "text" = "AGPL-3.0" }
|
||||
keywords = ["machine-learning", "deep-learning", "computer-vision", "ML", "DL", "AI", "YOLO", "YOLOv3", "YOLOv5", "YOLOv8", "HUB", "Ultralytics"]
|
||||
authors = [
|
||||
{ name = "Glenn Jocher" },
|
||||
{ name = "Ayush Chaurasia" },
|
||||
{ name = "Jing Qiu" }
|
||||
]
|
||||
maintainers = [
|
||||
{ name = "Glenn Jocher" },
|
||||
{ name = "Ayush Chaurasia" },
|
||||
{ name = "Jing Qiu" }
|
||||
]
|
||||
classifiers = [
|
||||
"Development Status :: 4 - Beta",
|
||||
"Intended Audience :: Developers",
|
||||
"Intended Audience :: Education",
|
||||
"Intended Audience :: Science/Research",
|
||||
"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3.8",
|
||||
"Programming Language :: Python :: 3.9",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Programming Language :: Python :: 3.11",
|
||||
"Topic :: Software Development",
|
||||
"Topic :: Scientific/Engineering",
|
||||
"Topic :: Scientific/Engineering :: Artificial Intelligence",
|
||||
"Topic :: Scientific/Engineering :: Image Recognition",
|
||||
"Operating System :: POSIX :: Linux",
|
||||
"Operating System :: MacOS",
|
||||
"Operating System :: Microsoft :: Windows",
|
||||
]
|
||||
|
||||
# Required dependencies ------------------------------------------------------------------------------------------------
|
||||
dependencies = [
|
||||
"matplotlib>=3.3.0",
|
||||
"numpy>=1.22.2",
|
||||
"opencv-python>=4.6.0",
|
||||
"pillow>=7.1.2",
|
||||
"pyyaml>=5.3.1",
|
||||
"requests>=2.23.0",
|
||||
"scipy>=1.4.1",
|
||||
"torch>=1.8.0",
|
||||
"torchvision>=0.9.0",
|
||||
"tqdm>=4.64.0", # progress bars
|
||||
"psutil", # system utilization
|
||||
"py-cpuinfo", # display CPU info
|
||||
"thop>=0.1.1", # FLOPs computation
|
||||
"pandas>=1.1.4",
|
||||
"seaborn>=0.11.0", # plotting
|
||||
"packaging", # general utilities
|
||||
"ultralytics>=8.2.64"
|
||||
]
|
||||
|
||||
# Optional dependencies ------------------------------------------------------------------------------------------------
|
||||
[project.optional-dependencies]
|
||||
dev = [
|
||||
"ipython",
|
||||
"check-manifest",
|
||||
"pytest",
|
||||
"pytest-cov",
|
||||
"coverage[toml]",
|
||||
"mkdocs-material",
|
||||
"mkdocstrings[python]",
|
||||
"mkdocs-ultralytics-plugin>=0.0.34", # for meta descriptions and images, dates and authors
|
||||
]
|
||||
export = [
|
||||
"onnx>=1.12.0", # ONNX export
|
||||
"coremltools>=7.0; platform_system != 'Windows'", # CoreML only supported on macOS and Linux
|
||||
"openvino-dev>=2023.0", # OpenVINO export
|
||||
"tensorflow>=2.0.0,<=2.19.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
|
||||
"tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow
|
||||
"keras>=3.5.0,<=3.12.0", # pin to avoid XNNPACK errors
|
||||
]
|
||||
# tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos)
|
||||
# tflite-support # for TFLite model metadata
|
||||
# scikit-learn==0.19.2 # CoreML quantization
|
||||
# nvidia-pyindex # TensorRT export
|
||||
# nvidia-tensorrt # TensorRT export
|
||||
logging = [
|
||||
"comet", # https://docs.ultralytics.com/integrations/comet/
|
||||
"tensorboard>=2.13.0",
|
||||
"dvclive>=2.12.0",
|
||||
]
|
||||
extra = [
|
||||
"ipython", # interactive notebook
|
||||
"albumentations>=1.0.3", # training augmentations
|
||||
"pycocotools>=2.0.6", # COCO mAP
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
"Bug Reports" = "https://github.com/ultralytics/yolov5/issues"
|
||||
"Funding" = "https://ultralytics.com"
|
||||
"Source" = "https://github.com/ultralytics/yolov5/"
|
||||
|
||||
# Tools settings -------------------------------------------------------------------------------------------------------
|
||||
[tool.pytest]
|
||||
norecursedirs = [".git", "dist", "build"]
|
||||
addopts = "--doctest-modules --durations=30 --color=yes"
|
||||
|
||||
[tool.isort]
|
||||
line_length = 120
|
||||
multi_line_output = 0
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 120
|
||||
|
||||
[tool.docformatter]
|
||||
wrap-summaries = 120
|
||||
wrap-descriptions = 120
|
||||
in-place = true
|
||||
pre-summary-newline = true
|
||||
close-quotes-on-newline = true
|
||||
|
||||
[tool.codespell]
|
||||
ignore-words-list = "crate,nd,strack,dota,ane,segway,fo,gool,winn,commend"
|
||||
skip = '*.csv,*venv*,docs/??/,docs/mkdocs_??.yml'
|
||||
51
third_party/yolov5/requirements.txt
vendored
Normal file
51
third_party/yolov5/requirements.txt
vendored
Normal file
@ -0,0 +1,51 @@
|
||||
# YOLOv5 requirements
|
||||
# Usage: pip install -r requirements.txt
|
||||
|
||||
# Base ------------------------------------------------------------------------
|
||||
gitpython>=3.1.30
|
||||
matplotlib>=3.3
|
||||
numpy>=1.23.5
|
||||
opencv-python>=4.1.1
|
||||
pillow>=10.3.0
|
||||
psutil # system resources
|
||||
PyYAML>=5.3.1
|
||||
requests>=2.32.2
|
||||
scipy>=1.4.1
|
||||
thop>=0.1.1 # FLOPs computation
|
||||
torch>=1.8.0 # see https://pytorch.org/get-started/locally (recommended)
|
||||
torchvision>=0.9.0
|
||||
tqdm>=4.66.3
|
||||
ultralytics>=8.2.64 # https://ultralytics.com
|
||||
# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012
|
||||
|
||||
# Logging ---------------------------------------------------------------------
|
||||
# tensorboard>=2.4.1
|
||||
# clearml>=1.2.0
|
||||
# comet
|
||||
|
||||
# Plotting --------------------------------------------------------------------
|
||||
pandas>=1.1.4
|
||||
seaborn>=0.11.0
|
||||
|
||||
# Export ----------------------------------------------------------------------
|
||||
# coremltools>=6.0 # CoreML export
|
||||
# onnx>=1.10.0 # ONNX export
|
||||
# onnx-simplifier>=0.4.1 # ONNX simplifier
|
||||
# nvidia-pyindex # TensorRT export
|
||||
# nvidia-tensorrt # TensorRT export
|
||||
# scikit-learn<=1.1.2 # CoreML quantization
|
||||
# tensorflow>=2.4.0,<=2.13.1 # TF exports (-cpu, -aarch64, -macos)
|
||||
# tensorflowjs>=3.9.0 # TF.js export
|
||||
# openvino-dev>=2023.0 # OpenVINO export
|
||||
|
||||
# Deploy ----------------------------------------------------------------------
|
||||
packaging # Migration of deprecated pkg_resources packages
|
||||
setuptools>=70.0.0 # Snyk vulnerability fix
|
||||
# tritonclient[all]~=2.24.0
|
||||
|
||||
# Extras ----------------------------------------------------------------------
|
||||
# ipython # interactive notebook
|
||||
# mss # screenshots
|
||||
# albumentations>=1.0.3
|
||||
# pycocotools>=2.0.6 # COCO mAP
|
||||
urllib3>=2.6.0 ; python_version > "3.8" # not directly required, pinned by Snyk to avoid a vulnerability
|
||||
307
third_party/yolov5/segment/predict.py
vendored
Normal file
307
third_party/yolov5/segment/predict.py
vendored
Normal file
@ -0,0 +1,307 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
|
||||
|
||||
Usage - sources:
|
||||
$ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/LNwODJXcvt4' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
|
||||
Usage - formats:
|
||||
$ python segment/predict.py --weights yolov5s-seg.pt # PyTorch
|
||||
yolov5s-seg.torchscript # TorchScript
|
||||
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-seg_openvino_model # OpenVINO
|
||||
yolov5s-seg.engine # TensorRT
|
||||
yolov5s-seg.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-seg_saved_model # TensorFlow SavedModel
|
||||
yolov5s-seg.pb # TensorFlow GraphDef
|
||||
yolov5s-seg.tflite # TensorFlow Lite
|
||||
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-seg_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from ultralytics.utils.plotting import Annotator, colors, save_one_box
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
Profile,
|
||||
check_file,
|
||||
check_img_size,
|
||||
check_imshow,
|
||||
check_requirements,
|
||||
colorstr,
|
||||
cv2,
|
||||
increment_path,
|
||||
non_max_suppression,
|
||||
print_args,
|
||||
scale_boxes,
|
||||
scale_segments,
|
||||
strip_optimizer,
|
||||
)
|
||||
from utils.segment.general import masks2segments, process_mask, process_mask_native
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
weights=ROOT / "yolov5s-seg.pt", # model.pt path(s)
|
||||
source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
|
||||
data=ROOT / "data/coco128.yaml", # dataset.yaml path
|
||||
imgsz=(640, 640), # inference size (height, width)
|
||||
conf_thres=0.25, # confidence threshold
|
||||
iou_thres=0.45, # NMS IOU threshold
|
||||
max_det=1000, # maximum detections per image
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
view_img=False, # show results
|
||||
save_txt=False, # save results to *.txt
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_crop=False, # save cropped prediction boxes
|
||||
nosave=False, # do not save images/videos
|
||||
classes=None, # filter by class: --class 0, or --class 0 2 3
|
||||
agnostic_nms=False, # class-agnostic NMS
|
||||
augment=False, # augmented inference
|
||||
visualize=False, # visualize features
|
||||
update=False, # update all models
|
||||
project=ROOT / "runs/predict-seg", # save results to project/name
|
||||
name="exp", # save results to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
line_thickness=3, # bounding box thickness (pixels)
|
||||
hide_labels=False, # hide labels
|
||||
hide_conf=False, # hide confidences
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
vid_stride=1, # video frame-rate stride
|
||||
retina_masks=False,
|
||||
):
|
||||
"""Run YOLOv5 segmentation inference on diverse sources including images, videos, directories, and streams."""
|
||||
source = str(source)
|
||||
save_img = not nosave and not source.endswith(".txt") # save inference images
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
|
||||
webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
|
||||
screenshot = source.lower().startswith("screen")
|
||||
if is_url and is_file:
|
||||
source = check_file(source) # download
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
device = select_device(device)
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
|
||||
# Dataloader
|
||||
bs = 1 # batch_size
|
||||
if webcam:
|
||||
view_img = check_imshow(warn=True)
|
||||
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||||
bs = len(dataset)
|
||||
elif screenshot:
|
||||
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||||
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||
|
||||
# Run inference
|
||||
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
|
||||
seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
with dt[0]:
|
||||
im = torch.from_numpy(im).to(model.device)
|
||||
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred, proto = model(im, augment=augment, visualize=visualize)[:2]
|
||||
|
||||
# NMS
|
||||
with dt[2]:
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32)
|
||||
|
||||
# Second-stage classifier (optional)
|
||||
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||||
|
||||
# Process predictions
|
||||
for i, det in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if webcam: # batch_size >= 1
|
||||
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||
s += f"{i}: "
|
||||
else:
|
||||
p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
|
||||
s += "{:g}x{:g} ".format(*im.shape[2:]) # print string
|
||||
imc = im0.copy() if save_crop else im0 # for save_crop
|
||||
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||||
if len(det):
|
||||
if retina_masks:
|
||||
# scale bbox first the crop masks
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
|
||||
masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC
|
||||
else:
|
||||
masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
|
||||
|
||||
# Segments
|
||||
if save_txt:
|
||||
segments = [
|
||||
scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
|
||||
for x in reversed(masks2segments(masks))
|
||||
]
|
||||
|
||||
# Print results
|
||||
for c in det[:, 5].unique():
|
||||
n = (det[:, 5] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
|
||||
# Mask plotting
|
||||
annotator.masks(
|
||||
masks,
|
||||
colors=[colors(x, True) for x in det[:, 5]],
|
||||
im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous()
|
||||
/ 255
|
||||
if retina_masks
|
||||
else im[i],
|
||||
)
|
||||
|
||||
# Write results
|
||||
for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
|
||||
if save_txt: # Write to file
|
||||
seg = segments[j].reshape(-1) # (n,2) to (n*2)
|
||||
line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format
|
||||
with open(f"{txt_path}.txt", "a") as f:
|
||||
f.write(("%g " * len(line)).rstrip() % line + "\n")
|
||||
|
||||
if save_img or save_crop or view_img: # Add bbox to image
|
||||
c = int(cls) # integer class
|
||||
label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
|
||||
annotator.box_label(xyxy, label, color=colors(c, True))
|
||||
# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
|
||||
if save_crop:
|
||||
save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
|
||||
|
||||
# Stream results
|
||||
im0 = annotator.result()
|
||||
if view_img:
|
||||
if platform.system() == "Linux" and p not in windows:
|
||||
windows.append(p)
|
||||
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||||
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||||
cv2.imshow(str(p), im0)
|
||||
if cv2.waitKey(1) == ord("q"): # 1 millisecond
|
||||
exit()
|
||||
|
||||
# Save results (image with detections)
|
||||
if save_img:
|
||||
if dataset.mode == "image":
|
||||
cv2.imwrite(save_path, im0)
|
||||
else: # 'video' or 'stream'
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms")
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
if update:
|
||||
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses command-line options for YOLOv5 inference including model paths, data sources, inference settings, and
|
||||
output preferences.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)")
|
||||
parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
|
||||
parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")
|
||||
parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")
|
||||
parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--view-img", action="store_true", help="show results")
|
||||
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||||
parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
|
||||
parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
|
||||
parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
|
||||
parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3")
|
||||
parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS")
|
||||
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||||
parser.add_argument("--visualize", action="store_true", help="visualize features")
|
||||
parser.add_argument("--update", action="store_true", help="update all models")
|
||||
parser.add_argument("--project", default=ROOT / "runs/predict-seg", help="save results to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save results to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
|
||||
parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
|
||||
parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
|
||||
parser.add_argument("--retina-masks", action="store_true", help="whether to plot masks in native resolution")
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 model inference with given options, checking for requirements before launching."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
763
third_party/yolov5/segment/train.py
vendored
Normal file
763
third_party/yolov5/segment/train.py
vendored
Normal file
@ -0,0 +1,763 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5
|
||||
release.
|
||||
|
||||
Usage - Single-GPU training:
|
||||
$ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended)
|
||||
$ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch
|
||||
|
||||
Usage - Multi-GPU DDP training:
|
||||
$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
|
||||
|
||||
Models: https://github.com/ultralytics/yolov5/tree/master/models
|
||||
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
|
||||
Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import yaml
|
||||
from torch.optim import lr_scheduler
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from ultralytics.utils.patches import torch_load
|
||||
|
||||
import segment.val as validate # for end-of-epoch mAP
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import SegmentationModel
|
||||
from utils.autoanchor import check_anchors
|
||||
from utils.autobatch import check_train_batch_size
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.downloads import attempt_download, is_url
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
TQDM_BAR_FORMAT,
|
||||
check_amp,
|
||||
check_dataset,
|
||||
check_file,
|
||||
check_git_info,
|
||||
check_git_status,
|
||||
check_img_size,
|
||||
check_requirements,
|
||||
check_suffix,
|
||||
check_yaml,
|
||||
colorstr,
|
||||
get_latest_run,
|
||||
increment_path,
|
||||
init_seeds,
|
||||
intersect_dicts,
|
||||
labels_to_class_weights,
|
||||
labels_to_image_weights,
|
||||
one_cycle,
|
||||
print_args,
|
||||
print_mutation,
|
||||
strip_optimizer,
|
||||
yaml_save,
|
||||
)
|
||||
from utils.loggers import GenericLogger
|
||||
from utils.plots import plot_evolve, plot_labels
|
||||
from utils.segment.dataloaders import create_dataloader
|
||||
from utils.segment.loss import ComputeLoss
|
||||
from utils.segment.metrics import KEYS, fitness
|
||||
from utils.segment.plots import plot_images_and_masks, plot_results_with_masks
|
||||
from utils.torch_utils import (
|
||||
EarlyStopping,
|
||||
ModelEMA,
|
||||
de_parallel,
|
||||
select_device,
|
||||
smart_DDP,
|
||||
smart_optimizer,
|
||||
smart_resume,
|
||||
torch_distributed_zero_first,
|
||||
)
|
||||
|
||||
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
|
||||
RANK = int(os.getenv("RANK", -1))
|
||||
WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
|
||||
GIT_INFO = check_git_info()
|
||||
|
||||
|
||||
def train(hyp, opt, device, callbacks):
|
||||
"""Trains the YOLOv5 model on a dataset, managing hyperparameters, model optimization, logging, and validation.
|
||||
|
||||
`hyp` is path/to/hyp.yaml or hyp dictionary.
|
||||
"""
|
||||
(
|
||||
save_dir,
|
||||
epochs,
|
||||
batch_size,
|
||||
weights,
|
||||
single_cls,
|
||||
evolve,
|
||||
data,
|
||||
cfg,
|
||||
resume,
|
||||
noval,
|
||||
nosave,
|
||||
workers,
|
||||
freeze,
|
||||
mask_ratio,
|
||||
) = (
|
||||
Path(opt.save_dir),
|
||||
opt.epochs,
|
||||
opt.batch_size,
|
||||
opt.weights,
|
||||
opt.single_cls,
|
||||
opt.evolve,
|
||||
opt.data,
|
||||
opt.cfg,
|
||||
opt.resume,
|
||||
opt.noval,
|
||||
opt.nosave,
|
||||
opt.workers,
|
||||
opt.freeze,
|
||||
opt.mask_ratio,
|
||||
)
|
||||
# callbacks.run('on_pretrain_routine_start')
|
||||
|
||||
# Directories
|
||||
w = save_dir / "weights" # weights dir
|
||||
(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
|
||||
last, best = w / "last.pt", w / "best.pt"
|
||||
|
||||
# Hyperparameters
|
||||
if isinstance(hyp, str):
|
||||
with open(hyp, errors="ignore") as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items()))
|
||||
opt.hyp = hyp.copy() # for saving hyps to checkpoints
|
||||
|
||||
# Save run settings
|
||||
if not evolve:
|
||||
yaml_save(save_dir / "hyp.yaml", hyp)
|
||||
yaml_save(save_dir / "opt.yaml", vars(opt))
|
||||
|
||||
# Loggers
|
||||
data_dict = None
|
||||
if RANK in {-1, 0}:
|
||||
logger = GenericLogger(opt=opt, console_logger=LOGGER)
|
||||
|
||||
# Config
|
||||
plots = not evolve and not opt.noplots # create plots
|
||||
overlap = not opt.no_overlap
|
||||
cuda = device.type != "cpu"
|
||||
init_seeds(opt.seed + 1 + RANK, deterministic=True)
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
data_dict = data_dict or check_dataset(data) # check if None
|
||||
train_path, val_path = data_dict["train"], data_dict["val"]
|
||||
nc = 1 if single_cls else int(data_dict["nc"]) # number of classes
|
||||
names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names
|
||||
is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset
|
||||
|
||||
# Model
|
||||
check_suffix(weights, ".pt") # check weights
|
||||
pretrained = weights.endswith(".pt")
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
weights = attempt_download(weights) # download if not found locally
|
||||
ckpt = torch_load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak
|
||||
model = SegmentationModel(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device)
|
||||
exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys
|
||||
csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report
|
||||
else:
|
||||
model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create
|
||||
amp = check_amp(model) # check AMP
|
||||
|
||||
# Freeze
|
||||
freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = True # train all layers
|
||||
# v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
|
||||
if any(x in k for x in freeze):
|
||||
LOGGER.info(f"freezing {k}")
|
||||
v.requires_grad = False
|
||||
|
||||
# Image size
|
||||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||||
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
|
||||
|
||||
# Batch size
|
||||
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
|
||||
batch_size = check_train_batch_size(model, imgsz, amp)
|
||||
logger.update_params({"batch_size": batch_size})
|
||||
# loggers.on_params_update({"batch_size": batch_size})
|
||||
|
||||
# Optimizer
|
||||
nbs = 64 # nominal batch size
|
||||
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
|
||||
hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay
|
||||
optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"])
|
||||
|
||||
# Scheduler
|
||||
if opt.cos_lr:
|
||||
lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf']
|
||||
else:
|
||||
|
||||
def lf(x):
|
||||
"""Linear learning rate scheduler decreasing from 1 to hyp['lrf'] over 'epochs'."""
|
||||
return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear
|
||||
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if RANK in {-1, 0} else None
|
||||
|
||||
# Resume
|
||||
best_fitness, start_epoch = 0.0, 0
|
||||
if pretrained:
|
||||
if resume:
|
||||
best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
|
||||
del ckpt, csd
|
||||
|
||||
# DP mode
|
||||
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
|
||||
LOGGER.warning(
|
||||
"WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n"
|
||||
"See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started."
|
||||
)
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# SyncBatchNorm
|
||||
if opt.sync_bn and cuda and RANK != -1:
|
||||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
|
||||
LOGGER.info("Using SyncBatchNorm()")
|
||||
|
||||
# Trainloader
|
||||
train_loader, dataset = create_dataloader(
|
||||
train_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
augment=True,
|
||||
cache=None if opt.cache == "val" else opt.cache,
|
||||
rect=opt.rect,
|
||||
rank=LOCAL_RANK,
|
||||
workers=workers,
|
||||
image_weights=opt.image_weights,
|
||||
quad=opt.quad,
|
||||
prefix=colorstr("train: "),
|
||||
shuffle=True,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap_mask=overlap,
|
||||
)
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
mlc = int(labels[:, 0].max()) # max label class
|
||||
assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}"
|
||||
|
||||
# Process 0
|
||||
if RANK in {-1, 0}:
|
||||
val_loader = create_dataloader(
|
||||
val_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE * 2,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
cache=None if noval else opt.cache,
|
||||
rect=True,
|
||||
rank=-1,
|
||||
workers=workers * 2,
|
||||
pad=0.5,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap_mask=overlap,
|
||||
prefix=colorstr("val: "),
|
||||
)[0]
|
||||
|
||||
if not resume:
|
||||
if not opt.noautoanchor:
|
||||
check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor
|
||||
model.half().float() # pre-reduce anchor precision
|
||||
|
||||
if plots:
|
||||
plot_labels(labels, names, save_dir)
|
||||
# callbacks.run('on_pretrain_routine_end', labels, names)
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = smart_DDP(model)
|
||||
|
||||
# Model attributes
|
||||
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
|
||||
hyp["box"] *= 3 / nl # scale to layers
|
||||
hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers
|
||||
hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
|
||||
hyp["label_smoothing"] = opt.label_smoothing
|
||||
model.nc = nc # attach number of classes to model
|
||||
model.hyp = hyp # attach hyperparameters to model
|
||||
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
|
||||
model.names = names
|
||||
|
||||
# Start training
|
||||
t0 = time.time()
|
||||
nb = len(train_loader) # number of batches
|
||||
nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
|
||||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||||
last_opt_step = -1
|
||||
maps = np.zeros(nc) # mAP per class
|
||||
results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||||
scheduler.last_epoch = start_epoch - 1 # do not move
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=amp)
|
||||
stopper, stop = EarlyStopping(patience=opt.patience), False
|
||||
compute_loss = ComputeLoss(model, overlap=overlap) # init loss class
|
||||
# callbacks.run('on_train_start')
|
||||
LOGGER.info(
|
||||
f"Image sizes {imgsz} train, {imgsz} val\n"
|
||||
f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n"
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f"Starting training for {epochs} epochs..."
|
||||
)
|
||||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||||
# callbacks.run('on_train_epoch_start')
|
||||
model.train()
|
||||
|
||||
# Update image weights (optional, single-GPU only)
|
||||
if opt.image_weights:
|
||||
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
|
||||
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
|
||||
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
|
||||
|
||||
# Update mosaic border (optional)
|
||||
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
|
||||
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
|
||||
|
||||
mloss = torch.zeros(4, device=device) # mean losses
|
||||
if RANK != -1:
|
||||
train_loader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(train_loader)
|
||||
LOGGER.info(
|
||||
("\n" + "%11s" * 8)
|
||||
% ("Epoch", "GPU_mem", "box_loss", "seg_loss", "obj_loss", "cls_loss", "Instances", "Size")
|
||||
)
|
||||
if RANK in {-1, 0}:
|
||||
pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------
|
||||
# callbacks.run('on_train_batch_start')
|
||||
ni = i + nb * epoch # number integrated batches (since train start)
|
||||
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
|
||||
|
||||
# Warmup
|
||||
if ni <= nw:
|
||||
xi = [0, nw] # x interp
|
||||
# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
|
||||
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
|
||||
for j, x in enumerate(optimizer.param_groups):
|
||||
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
|
||||
x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)])
|
||||
if "momentum" in x:
|
||||
x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]])
|
||||
|
||||
# Multi-scale
|
||||
if opt.multi_scale:
|
||||
sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size
|
||||
sf = sz / max(imgs.shape[2:]) # scale factor
|
||||
if sf != 1:
|
||||
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
|
||||
imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
|
||||
|
||||
# Forward
|
||||
with torch.cuda.amp.autocast(amp):
|
||||
pred = model(imgs) # forward
|
||||
loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float())
|
||||
if RANK != -1:
|
||||
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
|
||||
if opt.quad:
|
||||
loss *= 4.0
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
|
||||
if ni - last_opt_step >= accumulate:
|
||||
scaler.unscale_(optimizer) # unscale gradients
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
|
||||
scaler.step(optimizer) # optimizer.step
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
last_opt_step = ni
|
||||
|
||||
# Log
|
||||
if RANK in {-1, 0}:
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
|
||||
pbar.set_description(
|
||||
("%11s" * 2 + "%11.4g" * 6)
|
||||
% (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||||
)
|
||||
# callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths)
|
||||
# if callbacks.stop_training:
|
||||
# return
|
||||
|
||||
# Mosaic plots
|
||||
if plots:
|
||||
if ni < 3:
|
||||
plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg")
|
||||
if ni == 10:
|
||||
files = sorted(save_dir.glob("train*.jpg"))
|
||||
logger.log_images(files, "Mosaics", epoch)
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
|
||||
# Scheduler
|
||||
lr = [x["lr"] for x in optimizer.param_groups] # for loggers
|
||||
scheduler.step()
|
||||
|
||||
if RANK in {-1, 0}:
|
||||
# mAP
|
||||
# callbacks.run('on_train_epoch_end', epoch=epoch)
|
||||
ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"])
|
||||
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
|
||||
if not noval or final_epoch: # Calculate mAP
|
||||
results, maps, _ = validate.run(
|
||||
data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
half=amp,
|
||||
model=ema.ema,
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
plots=False,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap=overlap,
|
||||
)
|
||||
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||||
stop = stopper(epoch=epoch, fitness=fi) # early stop check
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
log_vals = list(mloss) + list(results) + lr
|
||||
# callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
|
||||
# Log val metrics and media
|
||||
metrics_dict = dict(zip(KEYS, log_vals))
|
||||
logger.log_metrics(metrics_dict, epoch)
|
||||
|
||||
# Save model
|
||||
if (not nosave) or (final_epoch and not evolve): # if save
|
||||
ckpt = {
|
||||
"epoch": epoch,
|
||||
"best_fitness": best_fitness,
|
||||
"model": deepcopy(de_parallel(model)).half(),
|
||||
"ema": deepcopy(ema.ema).half(),
|
||||
"updates": ema.updates,
|
||||
"optimizer": optimizer.state_dict(),
|
||||
"opt": vars(opt),
|
||||
"git": GIT_INFO, # {remote, branch, commit} if a git repo
|
||||
"date": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fi:
|
||||
torch.save(ckpt, best)
|
||||
if opt.save_period > 0 and epoch % opt.save_period == 0:
|
||||
torch.save(ckpt, w / f"epoch{epoch}.pt")
|
||||
logger.log_model(w / f"epoch{epoch}.pt")
|
||||
del ckpt
|
||||
# callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
|
||||
|
||||
# EarlyStopping
|
||||
if RANK != -1: # if DDP training
|
||||
broadcast_list = [stop if RANK == 0 else None]
|
||||
dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
|
||||
if RANK != 0:
|
||||
stop = broadcast_list[0]
|
||||
if stop:
|
||||
break # must break all DDP ranks
|
||||
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training -----------------------------------------------------------------------------------------------------
|
||||
if RANK in {-1, 0}:
|
||||
LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.")
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if f is best:
|
||||
LOGGER.info(f"\nValidating {f}...")
|
||||
results, _, _ = validate.run(
|
||||
data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=attempt_load(f, device).half(),
|
||||
iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
save_json=is_coco,
|
||||
verbose=True,
|
||||
plots=plots,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss,
|
||||
mask_downsample_ratio=mask_ratio,
|
||||
overlap=overlap,
|
||||
) # val best model with plots
|
||||
if is_coco:
|
||||
# callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
|
||||
metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr))
|
||||
logger.log_metrics(metrics_dict, epoch)
|
||||
|
||||
# callbacks.run('on_train_end', last, best, epoch, results)
|
||||
# on train end callback using genericLogger
|
||||
logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs)
|
||||
if not opt.evolve:
|
||||
logger.log_model(best, epoch)
|
||||
if plots:
|
||||
plot_results_with_masks(file=save_dir / "results.csv") # save results.png
|
||||
files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))]
|
||||
files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
||||
logger.log_images(files, "Results", epoch + 1)
|
||||
logger.log_images(sorted(save_dir.glob("val*.jpg")), "Validation", epoch + 1)
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
|
||||
|
||||
def parse_opt(known=False):
|
||||
"""Parses command line arguments for training configurations, returning parsed arguments.
|
||||
|
||||
Supports both known and unknown args.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s-seg.pt", help="initial weights path")
|
||||
parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path")
|
||||
parser.add_argument("--epochs", type=int, default=100, help="total training epochs")
|
||||
parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)")
|
||||
parser.add_argument("--rect", action="store_true", help="rectangular training")
|
||||
parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training")
|
||||
parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
|
||||
parser.add_argument("--noval", action="store_true", help="only validate final epoch")
|
||||
parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor")
|
||||
parser.add_argument("--noplots", action="store_true", help="save no plot files")
|
||||
parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations")
|
||||
parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
|
||||
parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk")
|
||||
parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%")
|
||||
parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class")
|
||||
parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer")
|
||||
parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--project", default=ROOT / "runs/train-seg", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--quad", action="store_true", help="quad dataloader")
|
||||
parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler")
|
||||
parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon")
|
||||
parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)")
|
||||
parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2")
|
||||
parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)")
|
||||
parser.add_argument("--seed", type=int, default=0, help="Global training seed")
|
||||
parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
|
||||
|
||||
# Instance Segmentation Args
|
||||
parser.add_argument("--mask-ratio", type=int, default=4, help="Downsample the truth masks to saving memory")
|
||||
parser.add_argument("--no-overlap", action="store_true", help="Overlap masks train faster at slightly less mAP")
|
||||
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
def main(opt, callbacks=Callbacks()):
|
||||
"""Initializes training or evolution of YOLOv5 models based on provided configuration and options."""
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
check_git_status()
|
||||
check_requirements(ROOT / "requirements.txt")
|
||||
|
||||
# Resume
|
||||
if opt.resume and not opt.evolve: # resume from specified or most recent last.pt
|
||||
last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
|
||||
opt_yaml = last.parent.parent / "opt.yaml" # train options yaml
|
||||
opt_data = opt.data # original dataset
|
||||
if opt_yaml.is_file():
|
||||
with open(opt_yaml, errors="ignore") as f:
|
||||
d = yaml.safe_load(f)
|
||||
else:
|
||||
d = torch_load(last, map_location="cpu")["opt"]
|
||||
opt = argparse.Namespace(**d) # replace
|
||||
opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate
|
||||
if is_url(opt_data):
|
||||
opt.data = check_file(opt_data) # avoid HUB resume auth timeout
|
||||
else:
|
||||
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = (
|
||||
check_file(opt.data),
|
||||
check_yaml(opt.cfg),
|
||||
check_yaml(opt.hyp),
|
||||
str(opt.weights),
|
||||
str(opt.project),
|
||||
) # checks
|
||||
assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified"
|
||||
if opt.evolve:
|
||||
if opt.project == str(ROOT / "runs/train-seg"): # if default project name, rename to runs/evolve-seg
|
||||
opt.project = str(ROOT / "runs/evolve-seg")
|
||||
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
|
||||
if opt.name == "cfg":
|
||||
opt.name = Path(opt.cfg).stem # use model.yaml as name
|
||||
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
msg = "is not compatible with YOLOv5 Multi-GPU DDP training"
|
||||
assert not opt.image_weights, f"--image-weights {msg}"
|
||||
assert not opt.evolve, f"--evolve {msg}"
|
||||
assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size"
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device("cuda", LOCAL_RANK)
|
||||
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
|
||||
|
||||
# Train
|
||||
if not opt.evolve:
|
||||
train(opt.hyp, opt, device, callbacks)
|
||||
|
||||
# Evolve hyperparameters (optional)
|
||||
else:
|
||||
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
|
||||
meta = {
|
||||
"lr0": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
"lrf": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
"momentum": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
|
||||
"weight_decay": (1, 0.0, 0.001), # optimizer weight decay
|
||||
"warmup_epochs": (1, 0.0, 5.0), # warmup epochs (fractions ok)
|
||||
"warmup_momentum": (1, 0.0, 0.95), # warmup initial momentum
|
||||
"warmup_bias_lr": (1, 0.0, 0.2), # warmup initial bias lr
|
||||
"box": (1, 0.02, 0.2), # box loss gain
|
||||
"cls": (1, 0.2, 4.0), # cls loss gain
|
||||
"cls_pw": (1, 0.5, 2.0), # cls BCELoss positive_weight
|
||||
"obj": (1, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||||
"obj_pw": (1, 0.5, 2.0), # obj BCELoss positive_weight
|
||||
"iou_t": (0, 0.1, 0.7), # IoU training threshold
|
||||
"anchor_t": (1, 2.0, 8.0), # anchor-multiple threshold
|
||||
"anchors": (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||||
"fl_gamma": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||||
"hsv_h": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
"hsv_s": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
"hsv_v": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
"degrees": (1, 0.0, 45.0), # image rotation (+/- deg)
|
||||
"translate": (1, 0.0, 0.9), # image translation (+/- fraction)
|
||||
"scale": (1, 0.0, 0.9), # image scale (+/- gain)
|
||||
"shear": (1, 0.0, 10.0), # image shear (+/- deg)
|
||||
"perspective": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
"flipud": (1, 0.0, 1.0), # image flip up-down (probability)
|
||||
"fliplr": (0, 0.0, 1.0), # image flip left-right (probability)
|
||||
"mosaic": (1, 0.0, 1.0), # image mixup (probability)
|
||||
"mixup": (1, 0.0, 1.0), # image mixup (probability)
|
||||
"copy_paste": (1, 0.0, 1.0),
|
||||
} # segment copy-paste (probability)
|
||||
|
||||
with open(opt.hyp, errors="ignore") as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
if "anchors" not in hyp: # anchors commented in hyp.yaml
|
||||
hyp["anchors"] = 3
|
||||
if opt.noautoanchor:
|
||||
del hyp["anchors"], meta["anchors"]
|
||||
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
|
||||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||||
evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv"
|
||||
if opt.bucket:
|
||||
# download evolve.csv if exists
|
||||
subprocess.run(
|
||||
[
|
||||
"gsutil",
|
||||
"cp",
|
||||
f"gs://{opt.bucket}/evolve.csv",
|
||||
str(evolve_csv),
|
||||
]
|
||||
)
|
||||
|
||||
for _ in range(opt.evolve): # generations to evolve
|
||||
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
|
||||
# Select parent(s)
|
||||
parent = "single" # parent selection method: 'single' or 'weighted'
|
||||
x = np.loadtxt(evolve_csv, ndmin=2, delimiter=",", skiprows=1)
|
||||
n = min(5, len(x)) # number of previous results to consider
|
||||
x = x[np.argsort(-fitness(x))][:n] # top n mutations
|
||||
w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0)
|
||||
if parent == "single" or len(x) == 1:
|
||||
# x = x[random.randint(0, n - 1)] # random selection
|
||||
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
|
||||
elif parent == "weighted":
|
||||
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
|
||||
|
||||
# Mutate
|
||||
mp, s = 0.8, 0.2 # mutation probability, sigma
|
||||
npr = np.random
|
||||
npr.seed(int(time.time()))
|
||||
g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1
|
||||
ng = len(meta)
|
||||
v = np.ones(ng)
|
||||
while all(v == 1): # mutate until a change occurs (prevent duplicates)
|
||||
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
|
||||
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
|
||||
hyp[k] = float(x[i + 12] * v[i]) # mutate
|
||||
|
||||
# Constrain to limits
|
||||
for k, v in meta.items():
|
||||
hyp[k] = max(hyp[k], v[1]) # lower limit
|
||||
hyp[k] = min(hyp[k], v[2]) # upper limit
|
||||
hyp[k] = round(hyp[k], 5) # significant digits
|
||||
|
||||
# Train mutation
|
||||
results = train(hyp.copy(), opt, device, callbacks)
|
||||
callbacks = Callbacks()
|
||||
# Write mutation results
|
||||
print_mutation(KEYS[4:16], results, hyp.copy(), save_dir, opt.bucket)
|
||||
|
||||
# Plot results
|
||||
plot_evolve(evolve_csv)
|
||||
LOGGER.info(
|
||||
f"Hyperparameter evolution finished {opt.evolve} generations\n"
|
||||
f"Results saved to {colorstr('bold', save_dir)}\n"
|
||||
f"Usage example: $ python train.py --hyp {evolve_yaml}"
|
||||
)
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
"""Executes YOLOv5 training with given parameters, altering options programmatically; returns updated options.
|
||||
|
||||
Example: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
|
||||
"""
|
||||
opt = parse_opt(True)
|
||||
for k, v in kwargs.items():
|
||||
setattr(opt, k, v)
|
||||
main(opt)
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
598
third_party/yolov5/segment/tutorial.ipynb
vendored
Normal file
598
third_party/yolov5/segment/tutorial.ipynb
vendored
Normal file
@ -0,0 +1,598 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "t6MPjfT5NrKQ"
|
||||
},
|
||||
"source": [
|
||||
"<div align=\"center\">\n",
|
||||
" <a href=\"https://ultralytics.com/yolo\" target=\"_blank\">\n",
|
||||
" <img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\">\n",
|
||||
" </a>\n",
|
||||
"\n",
|
||||
" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
|
||||
"\n",
|
||||
" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
|
||||
" <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n",
|
||||
" <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
|
||||
" <a href=\"https://www.kaggle.com/models/ultralytics/yolo11\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
|
||||
"\n",
|
||||
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
|
||||
" <a href=\"https://community.ultralytics.com\"><img alt=\"Ultralytics Forums\" src=\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\"></a>\n",
|
||||
" <a href=\"https://reddit.com/r/ultralytics\"><img alt=\"Ultralytics Reddit\" src=\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue\"></a>\n",
|
||||
"</div>\n",
|
||||
"\n",
|
||||
"This **Ultralytics YOLOv5 Segmentation Colab Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\n",
|
||||
"\n",
|
||||
"Ultralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\n",
|
||||
"\n",
|
||||
"Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\n",
|
||||
"\n",
|
||||
"Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n",
|
||||
"\n",
|
||||
"<br>\n",
|
||||
"<div>\n",
|
||||
" <a href=\"https://www.youtube.com/watch?v=ZN3nRZT7b24\" target=\"_blank\">\n",
|
||||
" <img src=\"https://img.youtube.com/vi/ZN3nRZT7b24/maxresdefault.jpg\" alt=\"Ultralytics Video\" width=\"640\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);\">\n",
|
||||
" </a>\n",
|
||||
"\n",
|
||||
" <p style=\"font-size: 16px; font-family: Arial, sans-serif; color: #555;\">\n",
|
||||
" <strong>Watch: </strong> How to Train\n",
|
||||
" <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics</a>\n",
|
||||
" <a href=\"https://docs.ultralytics.com/models/yolo11/\">YOLO11</a> Model on Custom Dataset using Google Colab Notebook 🚀\n",
|
||||
" </p>\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "7mGmQbAO5pQb"
|
||||
},
|
||||
"source": [
|
||||
"# Setup\n",
|
||||
"\n",
|
||||
"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "wbvMlHd_QwMG",
|
||||
"outputId": "171b23f0-71b9-4cbf-b666-6fa2ecef70c8"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!git clone https://github.com/ultralytics/yolov5 # clone\n",
|
||||
"%cd yolov5\n",
|
||||
"%pip install -qr requirements.txt comet_ml # install\n",
|
||||
"\n",
|
||||
"import torch\n",
|
||||
"\n",
|
||||
"import utils\n",
|
||||
"\n",
|
||||
"display = utils.notebook_init() # checks"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "4JnkELT0cIJg"
|
||||
},
|
||||
"source": [
|
||||
"# 1. Predict\n",
|
||||
"\n",
|
||||
"`segment/predict.py` runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict`. Example inference sources are:\n",
|
||||
"\n",
|
||||
"```shell\n",
|
||||
"python segment/predict.py --source 0 # webcam\n",
|
||||
" img.jpg # image \n",
|
||||
" vid.mp4 # video\n",
|
||||
" screen # screenshot\n",
|
||||
" path/ # directory\n",
|
||||
" 'path/*.jpg' # glob\n",
|
||||
" 'https://youtu.be/LNwODJXcvt4' # YouTube\n",
|
||||
" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "zR9ZbuQCH7FX",
|
||||
"outputId": "3f67f1c7-f15e-4fa5-d251-967c3b77eaad"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n",
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"\n",
|
||||
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n",
|
||||
"100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
|
||||
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n",
|
||||
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n",
|
||||
"Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n",
|
||||
"Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n",
|
||||
"# display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "hkAzDWJ7cWTr"
|
||||
},
|
||||
"source": [
|
||||
" \n",
|
||||
"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/199030123-08c72f8d-6871-4116-8ed3-c373642cf28e.jpg\" width=\"600\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "0eq1SMWl6Sfn"
|
||||
},
|
||||
"source": [
|
||||
"# 2. Validate\n",
|
||||
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "WQPtK1QYVaD_",
|
||||
"outputId": "9d751d8c-bee8-4339-cf30-9854ca530449"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels-segments.zip ...\n",
|
||||
"Downloading http://images.cocodataset.org/zips/val2017.zip ...\n",
|
||||
"######################################################################## 100.0%\n",
|
||||
"######################################################################## 100.0%\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Download COCO val\n",
|
||||
"!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "X58w8JLpMnjH",
|
||||
"outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n",
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n",
|
||||
" all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n",
|
||||
"Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n",
|
||||
"Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Validate YOLOv5s-seg on COCO val\n",
|
||||
"!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ZY2VXXXu74w5"
|
||||
},
|
||||
"source": "# 3. Train\n\n<p align=\"\"><a href=\"https://platform.ultralytics.com\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n\nTrain a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n\n- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\nautomatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n<br><br>\n\nA **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic."
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "i3oKtE4g-aNn"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title Select YOLOv5 🚀 logger {run: 'auto'}\n",
|
||||
"logger = \"Comet\" # @param ['Comet', 'ClearML', 'TensorBoard']\n",
|
||||
"\n",
|
||||
"if logger == \"Comet\":\n",
|
||||
" %pip install -q comet_ml\n",
|
||||
" import comet_ml\n",
|
||||
"\n",
|
||||
" comet_ml.init()\n",
|
||||
"elif logger == \"ClearML\":\n",
|
||||
" %pip install -q clearml\n",
|
||||
" import clearml\n",
|
||||
"\n",
|
||||
" clearml.browser_login()\n",
|
||||
"elif logger == \"TensorBoard\":\n",
|
||||
" %load_ext tensorboard\n",
|
||||
" %tensorboard --logdir runs/train"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "1NcFxRcFdJ_O",
|
||||
"outputId": "3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n",
|
||||
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
|
||||
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
|
||||
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
|
||||
"\n",
|
||||
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n",
|
||||
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip to coco128-seg.zip...\n",
|
||||
"100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n",
|
||||
"Dataset download success ✅ (1.9s), saved to \u001b[1m/content/datasets\u001b[0m\n",
|
||||
"\n",
|
||||
" from n params module arguments \n",
|
||||
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
|
||||
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
|
||||
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
|
||||
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
|
||||
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
|
||||
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
|
||||
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
|
||||
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
|
||||
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
|
||||
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
|
||||
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
|
||||
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
|
||||
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
|
||||
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
|
||||
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
|
||||
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
|
||||
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
|
||||
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
|
||||
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
|
||||
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
|
||||
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
|
||||
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
|
||||
" 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\n",
|
||||
"Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n",
|
||||
"\n",
|
||||
"Transferred 367/367 items from yolov5s-seg.pt\n",
|
||||
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
|
||||
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n",
|
||||
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 98.90it/s]\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
|
||||
"Plotting labels to runs/train-seg/exp/labels.jpg... \n",
|
||||
"Image sizes 640 train, 640 val\n",
|
||||
"Using 2 dataloader workers\n",
|
||||
"Logging results to \u001b[1mruns/train-seg/exp\u001b[0m\n",
|
||||
"Starting training for 3 epochs...\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
|
||||
" 0/2 4.92G 0.0417 0.04646 0.06066 0.02126 192 640: 100% 8/8 [00:08<00:00, 1.10s/it]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.81it/s]\n",
|
||||
" all 128 929 0.737 0.649 0.715 0.492 0.719 0.617 0.658 0.408\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
|
||||
" 1/2 6.29G 0.04157 0.04503 0.05772 0.01777 208 640: 100% 8/8 [00:09<00:00, 1.21s/it]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.87it/s]\n",
|
||||
" all 128 929 0.756 0.674 0.738 0.506 0.725 0.64 0.68 0.422\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
|
||||
" 2/2 6.29G 0.0425 0.04793 0.06784 0.01863 161 640: 100% 8/8 [00:03<00:00, 2.02it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.88it/s]\n",
|
||||
" all 128 929 0.736 0.694 0.747 0.522 0.769 0.622 0.683 0.427\n",
|
||||
"\n",
|
||||
"3 epochs completed in 0.009 hours.\n",
|
||||
"Optimizer stripped from runs/train-seg/exp/weights/last.pt, 15.6MB\n",
|
||||
"Optimizer stripped from runs/train-seg/exp/weights/best.pt, 15.6MB\n",
|
||||
"\n",
|
||||
"Validating runs/train-seg/exp/weights/best.pt...\n",
|
||||
"Fusing layers... \n",
|
||||
"Model summary: 165 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.59s/it]\n",
|
||||
" all 128 929 0.738 0.694 0.746 0.522 0.759 0.625 0.682 0.426\n",
|
||||
" person 128 254 0.845 0.756 0.836 0.55 0.861 0.669 0.759 0.407\n",
|
||||
" bicycle 128 6 0.475 0.333 0.549 0.341 0.711 0.333 0.526 0.322\n",
|
||||
" car 128 46 0.612 0.565 0.539 0.257 0.555 0.435 0.477 0.171\n",
|
||||
" motorcycle 128 5 0.73 0.8 0.752 0.571 0.747 0.8 0.752 0.42\n",
|
||||
" airplane 128 6 1 0.943 0.995 0.732 0.92 0.833 0.839 0.555\n",
|
||||
" bus 128 7 0.677 0.714 0.722 0.653 0.711 0.714 0.722 0.593\n",
|
||||
" train 128 3 1 0.951 0.995 0.551 1 0.884 0.995 0.781\n",
|
||||
" truck 128 12 0.555 0.417 0.457 0.285 0.624 0.417 0.397 0.277\n",
|
||||
" boat 128 6 0.624 0.5 0.584 0.186 1 0.326 0.412 0.133\n",
|
||||
" traffic light 128 14 0.513 0.302 0.411 0.247 0.435 0.214 0.376 0.251\n",
|
||||
" stop sign 128 2 0.824 1 0.995 0.796 0.906 1 0.995 0.747\n",
|
||||
" bench 128 9 0.75 0.667 0.763 0.367 0.724 0.585 0.698 0.209\n",
|
||||
" bird 128 16 0.961 1 0.995 0.686 0.918 0.938 0.91 0.525\n",
|
||||
" cat 128 4 0.771 0.857 0.945 0.752 0.76 0.8 0.945 0.728\n",
|
||||
" dog 128 9 0.987 0.778 0.963 0.681 1 0.705 0.89 0.574\n",
|
||||
" horse 128 2 0.703 1 0.995 0.697 0.759 1 0.995 0.249\n",
|
||||
" elephant 128 17 0.916 0.882 0.93 0.691 0.811 0.765 0.829 0.537\n",
|
||||
" bear 128 1 0.664 1 0.995 0.995 0.701 1 0.995 0.895\n",
|
||||
" zebra 128 4 0.864 1 0.995 0.921 0.879 1 0.995 0.804\n",
|
||||
" giraffe 128 9 0.883 0.889 0.94 0.683 0.845 0.778 0.78 0.463\n",
|
||||
" backpack 128 6 1 0.59 0.701 0.372 1 0.474 0.52 0.252\n",
|
||||
" umbrella 128 18 0.654 0.839 0.887 0.52 0.517 0.556 0.427 0.229\n",
|
||||
" handbag 128 19 0.54 0.211 0.408 0.221 0.796 0.206 0.396 0.196\n",
|
||||
" tie 128 7 0.864 0.857 0.857 0.577 0.925 0.857 0.857 0.534\n",
|
||||
" suitcase 128 4 0.716 1 0.945 0.647 0.767 1 0.945 0.634\n",
|
||||
" frisbee 128 5 0.708 0.8 0.761 0.643 0.737 0.8 0.761 0.501\n",
|
||||
" skis 128 1 0.691 1 0.995 0.796 0.761 1 0.995 0.199\n",
|
||||
" snowboard 128 7 0.918 0.857 0.904 0.604 0.32 0.286 0.235 0.137\n",
|
||||
" sports ball 128 6 0.902 0.667 0.701 0.466 0.727 0.5 0.497 0.471\n",
|
||||
" kite 128 10 0.586 0.4 0.511 0.231 0.663 0.394 0.417 0.139\n",
|
||||
" baseball bat 128 4 0.359 0.5 0.401 0.169 0.631 0.5 0.526 0.133\n",
|
||||
" baseball glove 128 7 1 0.519 0.58 0.327 0.687 0.286 0.455 0.328\n",
|
||||
" skateboard 128 5 0.729 0.8 0.862 0.631 0.599 0.6 0.604 0.379\n",
|
||||
" tennis racket 128 7 0.57 0.714 0.645 0.448 0.608 0.714 0.645 0.412\n",
|
||||
" bottle 128 18 0.469 0.393 0.537 0.357 0.661 0.389 0.543 0.349\n",
|
||||
" wine glass 128 16 0.677 0.938 0.866 0.441 0.53 0.625 0.67 0.334\n",
|
||||
" cup 128 36 0.777 0.722 0.812 0.466 0.725 0.583 0.762 0.467\n",
|
||||
" fork 128 6 0.948 0.333 0.425 0.27 0.527 0.167 0.18 0.102\n",
|
||||
" knife 128 16 0.757 0.587 0.669 0.458 0.79 0.5 0.552 0.34\n",
|
||||
" spoon 128 22 0.74 0.364 0.559 0.269 0.925 0.364 0.513 0.213\n",
|
||||
" bowl 128 28 0.766 0.714 0.725 0.559 0.803 0.584 0.665 0.353\n",
|
||||
" banana 128 1 0.408 1 0.995 0.398 0.539 1 0.995 0.497\n",
|
||||
" sandwich 128 2 1 0 0.695 0.536 1 0 0.498 0.448\n",
|
||||
" orange 128 4 0.467 1 0.995 0.693 0.518 1 0.995 0.663\n",
|
||||
" broccoli 128 11 0.462 0.455 0.383 0.259 0.548 0.455 0.384 0.256\n",
|
||||
" carrot 128 24 0.631 0.875 0.77 0.533 0.757 0.909 0.853 0.499\n",
|
||||
" hot dog 128 2 0.555 1 0.995 0.995 0.578 1 0.995 0.796\n",
|
||||
" pizza 128 5 0.89 0.8 0.962 0.796 1 0.778 0.962 0.766\n",
|
||||
" donut 128 14 0.695 1 0.893 0.772 0.704 1 0.893 0.696\n",
|
||||
" cake 128 4 0.826 1 0.995 0.92 0.862 1 0.995 0.846\n",
|
||||
" chair 128 35 0.53 0.571 0.613 0.336 0.67 0.6 0.538 0.271\n",
|
||||
" couch 128 6 0.972 0.667 0.833 0.627 1 0.62 0.696 0.394\n",
|
||||
" potted plant 128 14 0.7 0.857 0.883 0.552 0.836 0.857 0.883 0.473\n",
|
||||
" bed 128 3 0.979 0.667 0.83 0.366 1 0 0.83 0.373\n",
|
||||
" dining table 128 13 0.775 0.308 0.505 0.364 0.644 0.231 0.25 0.0804\n",
|
||||
" toilet 128 2 0.836 1 0.995 0.846 0.887 1 0.995 0.797\n",
|
||||
" tv 128 2 0.6 1 0.995 0.846 0.655 1 0.995 0.896\n",
|
||||
" laptop 128 3 0.822 0.333 0.445 0.307 1 0 0.392 0.12\n",
|
||||
" mouse 128 2 1 0 0 0 1 0 0 0\n",
|
||||
" remote 128 8 0.745 0.5 0.62 0.459 0.821 0.5 0.624 0.449\n",
|
||||
" cell phone 128 8 0.686 0.375 0.502 0.272 0.488 0.25 0.28 0.132\n",
|
||||
" microwave 128 3 0.831 1 0.995 0.722 0.867 1 0.995 0.592\n",
|
||||
" oven 128 5 0.439 0.4 0.435 0.294 0.823 0.6 0.645 0.418\n",
|
||||
" sink 128 6 0.677 0.5 0.565 0.448 0.722 0.5 0.46 0.362\n",
|
||||
" refrigerator 128 5 0.533 0.8 0.783 0.524 0.558 0.8 0.783 0.527\n",
|
||||
" book 128 29 0.732 0.379 0.423 0.196 0.69 0.207 0.38 0.131\n",
|
||||
" clock 128 9 0.889 0.778 0.917 0.677 0.908 0.778 0.875 0.604\n",
|
||||
" vase 128 2 0.375 1 0.995 0.995 0.455 1 0.995 0.796\n",
|
||||
" scissors 128 1 1 0 0.0166 0.00166 1 0 0 0\n",
|
||||
" teddy bear 128 21 0.813 0.829 0.841 0.457 0.826 0.678 0.786 0.422\n",
|
||||
" toothbrush 128 5 0.806 1 0.995 0.733 0.991 1 0.995 0.628\n",
|
||||
"Results saved to \u001b[1mruns/train-seg/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Train YOLOv5s on COCO128 for 3 epochs\n",
|
||||
"!python segment/train.py --img 640 --batch 16 --epochs 3 --data coco128-seg.yaml --weights yolov5s-seg.pt --cache"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "15glLzbQx5u0"
|
||||
},
|
||||
"source": [
|
||||
"# 4. Visualize"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "nWOsI5wJR1o3"
|
||||
},
|
||||
"source": [
|
||||
"## Comet Logging and Visualization 🌟 NEW\n",
|
||||
"\n",
|
||||
"[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n",
|
||||
"\n",
|
||||
"Getting started is easy:\n",
|
||||
"```shell\n",
|
||||
"pip install comet_ml # 1. install\n",
|
||||
"export COMET_API_KEY=<Your API Key> # 2. paste API key\n",
|
||||
"python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n",
|
||||
"```\n",
|
||||
"To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
|
||||
"[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
|
||||
"\n",
|
||||
"<a href=\"https://bit.ly/yolov5-readme-comet2\">\n",
|
||||
"<img alt=\"Comet Dashboard\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\" width=\"1280\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Lay2WsTjNJzP"
|
||||
},
|
||||
"source": [
|
||||
"## ClearML Logging and Automation 🌟 NEW\n",
|
||||
"\n",
|
||||
"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
|
||||
"\n",
|
||||
"- `pip install clearml`\n",
|
||||
"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
|
||||
"\n",
|
||||
"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
|
||||
"\n",
|
||||
"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
|
||||
"\n",
|
||||
"<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
|
||||
"<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "-WPvRbS5Swl6"
|
||||
},
|
||||
"source": [
|
||||
"## Local Logging\n",
|
||||
"\n",
|
||||
"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
|
||||
"\n",
|
||||
"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
|
||||
"\n",
|
||||
"<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Zelyeqbyt3GD"
|
||||
},
|
||||
"source": [
|
||||
"# Environments\n",
|
||||
"\n",
|
||||
"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
|
||||
"\n",
|
||||
"- **Notebooks** with free GPU: <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a> <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/models/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
|
||||
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
|
||||
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
|
||||
"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "6Qu7Iesl0p54"
|
||||
},
|
||||
"source": [
|
||||
"# Status\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "IEijrePND_2I"
|
||||
},
|
||||
"source": [
|
||||
"# Appendix\n",
|
||||
"\n",
|
||||
"Additional content below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "GMusP4OAxFu6"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
|
||||
"\n",
|
||||
"model = torch.hub.load(\n",
|
||||
" \"ultralytics/yolov5\", \"yolov5s-seg\", force_reload=True, trust_repo=True\n",
|
||||
") # or yolov5n - yolov5x6 or custom\n",
|
||||
"im = \"https://ultralytics.com/images/zidane.jpg\" # file, Path, PIL.Image, OpenCV, nparray, list\n",
|
||||
"results = model(im) # inference\n",
|
||||
"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"name": "YOLOv5 Segmentation Tutorial",
|
||||
"provenance": [],
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
520
third_party/yolov5/segment/val.py
vendored
Normal file
520
third_party/yolov5/segment/val.py
vendored
Normal file
@ -0,0 +1,520 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Validate a trained YOLOv5 segment model on a segment dataset.
|
||||
|
||||
Usage:
|
||||
$ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)
|
||||
$ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments
|
||||
|
||||
Usage - formats:
|
||||
$ python segment/val.py --weights yolov5s-seg.pt # PyTorch
|
||||
yolov5s-seg.torchscript # TorchScript
|
||||
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s-seg_openvino_label # OpenVINO
|
||||
yolov5s-seg.engine # TensorRT
|
||||
yolov5s-seg.mlmodel # CoreML (macOS-only)
|
||||
yolov5s-seg_saved_model # TensorFlow SavedModel
|
||||
yolov5s-seg.pb # TensorFlow GraphDef
|
||||
yolov5s-seg.tflite # TensorFlow Lite
|
||||
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s-seg_paddle_model # PaddlePaddle
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
import torch.nn.functional as F
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from models.yolo import SegmentationModel
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
NUM_THREADS,
|
||||
TQDM_BAR_FORMAT,
|
||||
Profile,
|
||||
check_dataset,
|
||||
check_img_size,
|
||||
check_requirements,
|
||||
check_yaml,
|
||||
coco80_to_coco91_class,
|
||||
colorstr,
|
||||
increment_path,
|
||||
non_max_suppression,
|
||||
print_args,
|
||||
scale_boxes,
|
||||
xywh2xyxy,
|
||||
xyxy2xywh,
|
||||
)
|
||||
from utils.metrics import ConfusionMatrix, box_iou
|
||||
from utils.plots import output_to_target, plot_val_study
|
||||
from utils.segment.dataloaders import create_dataloader
|
||||
from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
|
||||
from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
|
||||
from utils.segment.plots import plot_images_and_masks
|
||||
from utils.torch_utils import de_parallel, select_device, smart_inference_mode
|
||||
|
||||
|
||||
def save_one_txt(predn, save_conf, shape, file):
|
||||
"""Saves detection results in txt format; includes class, xywh (normalized), optionally confidence if `save_conf` is
|
||||
True.
|
||||
"""
|
||||
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
for *xyxy, conf, cls in predn.tolist():
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||
with open(file, "a") as f:
|
||||
f.write(("%g " * len(line)).rstrip() % line + "\n")
|
||||
|
||||
|
||||
def save_one_json(predn, jdict, path, class_map, pred_masks):
|
||||
"""Saves a JSON file with detection results including bounding boxes, category IDs, scores, and segmentation masks.
|
||||
|
||||
Example JSON result: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}.
|
||||
"""
|
||||
from pycocotools.mask import encode
|
||||
|
||||
def single_encode(x):
|
||||
"""Encodes binary mask arrays into RLE (Run-Length Encoding) format for JSON serialization."""
|
||||
rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
|
||||
rle["counts"] = rle["counts"].decode("utf-8")
|
||||
return rle
|
||||
|
||||
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
|
||||
box = xyxy2xywh(predn[:, :4]) # xywh
|
||||
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
||||
pred_masks = np.transpose(pred_masks, (2, 0, 1))
|
||||
with ThreadPool(NUM_THREADS) as pool:
|
||||
rles = pool.map(single_encode, pred_masks)
|
||||
for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
|
||||
jdict.append(
|
||||
{
|
||||
"image_id": image_id,
|
||||
"category_id": class_map[int(p[5])],
|
||||
"bbox": [round(x, 3) for x in b],
|
||||
"score": round(p[4], 5),
|
||||
"segmentation": rles[i],
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
|
||||
"""Return correct prediction matrix.
|
||||
|
||||
Args:
|
||||
detections (array[N, 6]): x1, y1, x2, y2, conf, class
|
||||
labels (array[M, 5]): class, x1, y1, x2, y2
|
||||
|
||||
Returns:
|
||||
correct (array[N, 10]), for 10 IoU levels.
|
||||
"""
|
||||
if masks:
|
||||
if overlap:
|
||||
nl = len(labels)
|
||||
index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
|
||||
gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
|
||||
gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
|
||||
if gt_masks.shape[1:] != pred_masks.shape[1:]:
|
||||
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
|
||||
gt_masks = gt_masks.gt_(0.5)
|
||||
iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
|
||||
else: # boxes
|
||||
iou = box_iou(labels[:, 1:], detections[:, :4])
|
||||
|
||||
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
|
||||
correct_class = labels[:, 0:1] == detections[:, 5]
|
||||
for i in range(len(iouv)):
|
||||
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
|
||||
if x[0].shape[0]:
|
||||
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
|
||||
if x[0].shape[0] > 1:
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
|
||||
# matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
|
||||
correct[matches[:, 1].astype(int), i] = True
|
||||
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
data,
|
||||
weights=None, # model.pt path(s)
|
||||
batch_size=32, # batch size
|
||||
imgsz=640, # inference size (pixels)
|
||||
conf_thres=0.001, # confidence threshold
|
||||
iou_thres=0.6, # NMS IoU threshold
|
||||
max_det=300, # maximum detections per image
|
||||
task="val", # train, val, test, speed or study
|
||||
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
workers=8, # max dataloader workers (per RANK in DDP mode)
|
||||
single_cls=False, # treat as single-class dataset
|
||||
augment=False, # augmented inference
|
||||
verbose=False, # verbose output
|
||||
save_txt=False, # save results to *.txt
|
||||
save_hybrid=False, # save label+prediction hybrid results to *.txt
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_json=False, # save a COCO-JSON results file
|
||||
project=ROOT / "runs/val-seg", # save to project/name
|
||||
name="exp", # save to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=True, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
model=None,
|
||||
dataloader=None,
|
||||
save_dir=Path(""),
|
||||
plots=True,
|
||||
overlap=False,
|
||||
mask_downsample_ratio=1,
|
||||
compute_loss=None,
|
||||
callbacks=Callbacks(),
|
||||
):
|
||||
"""Validate a YOLOv5 segmentation model on specified dataset, producing metrics, plots, and optional JSON output."""
|
||||
if save_json:
|
||||
check_requirements("pycocotools>=2.0.6")
|
||||
process = process_mask_native # more accurate
|
||||
else:
|
||||
process = process_mask # faster
|
||||
|
||||
# Initialize/load model and set device
|
||||
training = model is not None
|
||||
if training: # called by train.py
|
||||
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
|
||||
half &= device.type != "cpu" # half precision only supported on CUDA
|
||||
model.half() if half else model.float()
|
||||
nm = de_parallel(model).model[-1].nm # number of masks
|
||||
else: # called directly
|
||||
device = select_device(device, batch_size=batch_size)
|
||||
|
||||
# Directories
|
||||
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||
(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||||
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
half = model.fp16 # FP16 supported on limited backends with CUDA
|
||||
nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
|
||||
if engine:
|
||||
batch_size = model.batch_size
|
||||
else:
|
||||
device = model.device
|
||||
if not (pt or jit):
|
||||
batch_size = 1 # export.py models default to batch-size 1
|
||||
LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
|
||||
|
||||
# Data
|
||||
data = check_dataset(data) # check
|
||||
|
||||
# Configure
|
||||
model.eval()
|
||||
cuda = device.type != "cpu"
|
||||
is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset
|
||||
nc = 1 if single_cls else int(data["nc"]) # number of classes
|
||||
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
|
||||
niou = iouv.numel()
|
||||
|
||||
# Dataloader
|
||||
if not training:
|
||||
if pt and not single_cls: # check --weights are trained on --data
|
||||
ncm = model.model.nc
|
||||
assert ncm == nc, (
|
||||
f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} "
|
||||
f"classes). Pass correct combination of --weights and --data that are trained together."
|
||||
)
|
||||
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
|
||||
pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks
|
||||
task = task if task in ("train", "val", "test") else "val" # path to train/val/test images
|
||||
dataloader = create_dataloader(
|
||||
data[task],
|
||||
imgsz,
|
||||
batch_size,
|
||||
stride,
|
||||
single_cls,
|
||||
pad=pad,
|
||||
rect=rect,
|
||||
workers=workers,
|
||||
prefix=colorstr(f"{task}: "),
|
||||
overlap_mask=overlap,
|
||||
mask_downsample_ratio=mask_downsample_ratio,
|
||||
)[0]
|
||||
|
||||
seen = 0
|
||||
confusion_matrix = ConfusionMatrix(nc=nc)
|
||||
names = model.names if hasattr(model, "names") else model.module.names # get class names
|
||||
if isinstance(names, (list, tuple)): # old format
|
||||
names = dict(enumerate(names))
|
||||
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
|
||||
s = ("%22s" + "%11s" * 10) % (
|
||||
"Class",
|
||||
"Images",
|
||||
"Instances",
|
||||
"Box(P",
|
||||
"R",
|
||||
"mAP50",
|
||||
"mAP50-95)",
|
||||
"Mask(P",
|
||||
"R",
|
||||
"mAP50",
|
||||
"mAP50-95)",
|
||||
)
|
||||
dt = Profile(device=device), Profile(device=device), Profile(device=device)
|
||||
metrics = Metrics()
|
||||
loss = torch.zeros(4, device=device)
|
||||
jdict, stats = [], []
|
||||
# callbacks.run('on_val_start')
|
||||
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
|
||||
# callbacks.run('on_val_batch_start')
|
||||
with dt[0]:
|
||||
if cuda:
|
||||
im = im.to(device, non_blocking=True)
|
||||
targets = targets.to(device)
|
||||
masks = masks.to(device)
|
||||
masks = masks.float()
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
nb, _, height, width = im.shape # batch size, channels, height, width
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
|
||||
|
||||
# Loss
|
||||
if compute_loss:
|
||||
loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls
|
||||
|
||||
# NMS
|
||||
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
|
||||
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
|
||||
with dt[2]:
|
||||
preds = non_max_suppression(
|
||||
preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm
|
||||
)
|
||||
|
||||
# Metrics
|
||||
plot_masks = [] # masks for plotting
|
||||
for si, (pred, proto) in enumerate(zip(preds, protos)):
|
||||
labels = targets[targets[:, 0] == si, 1:]
|
||||
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
|
||||
path, shape = Path(paths[si]), shapes[si][0]
|
||||
correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
|
||||
correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
|
||||
seen += 1
|
||||
|
||||
if npr == 0:
|
||||
if nl:
|
||||
stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
|
||||
if plots:
|
||||
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
|
||||
continue
|
||||
|
||||
# Masks
|
||||
midx = [si] if overlap else targets[:, 0] == si
|
||||
gt_masks = masks[midx]
|
||||
pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
|
||||
|
||||
# Predictions
|
||||
if single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn = pred.clone()
|
||||
scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
|
||||
scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
|
||||
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
|
||||
correct_bboxes = process_batch(predn, labelsn, iouv)
|
||||
correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)
|
||||
if plots:
|
||||
confusion_matrix.process_batch(predn, labelsn)
|
||||
stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
|
||||
|
||||
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
|
||||
if plots and batch_i < 3:
|
||||
plot_masks.append(pred_masks[:15]) # filter top 15 to plot
|
||||
|
||||
# Save/log
|
||||
if save_txt:
|
||||
save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
|
||||
if save_json:
|
||||
pred_masks = scale_image(
|
||||
im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]
|
||||
)
|
||||
save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
|
||||
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
|
||||
|
||||
# Plot images
|
||||
if plots and batch_i < 3:
|
||||
if len(plot_masks):
|
||||
plot_masks = torch.cat(plot_masks, dim=0)
|
||||
plot_images_and_masks(im, targets, masks, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names)
|
||||
plot_images_and_masks(
|
||||
im,
|
||||
output_to_target(preds, max_det=15),
|
||||
plot_masks,
|
||||
paths,
|
||||
save_dir / f"val_batch{batch_i}_pred.jpg",
|
||||
names,
|
||||
) # pred
|
||||
|
||||
# callbacks.run('on_val_batch_end')
|
||||
|
||||
# Compute metrics
|
||||
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
|
||||
if len(stats) and stats[0].any():
|
||||
results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)
|
||||
metrics.update(results)
|
||||
nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class
|
||||
|
||||
# Print results
|
||||
pf = "%22s" + "%11i" * 2 + "%11.3g" * 8 # print format
|
||||
LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
|
||||
if nt.sum() == 0:
|
||||
LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels")
|
||||
|
||||
# Print results per class
|
||||
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
|
||||
for i, c in enumerate(metrics.ap_class_index):
|
||||
LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))
|
||||
|
||||
# Print speeds
|
||||
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
|
||||
if not training:
|
||||
shape = (batch_size, 3, imgsz, imgsz)
|
||||
LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t)
|
||||
|
||||
# Plots
|
||||
if plots:
|
||||
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
|
||||
# callbacks.run('on_val_end')
|
||||
|
||||
mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()
|
||||
|
||||
# Save JSON
|
||||
if save_json and len(jdict):
|
||||
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights
|
||||
anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations
|
||||
pred_json = str(save_dir / f"{w}_predictions.json") # predictions
|
||||
LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...")
|
||||
with open(pred_json, "w") as f:
|
||||
json.dump(jdict, f)
|
||||
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
|
||||
anno = COCO(anno_json) # init annotations api
|
||||
pred = anno.loadRes(pred_json) # init predictions api
|
||||
results = []
|
||||
for eval in COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm"):
|
||||
if is_coco:
|
||||
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate
|
||||
eval.evaluate()
|
||||
eval.accumulate()
|
||||
eval.summarize()
|
||||
results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)
|
||||
map_bbox, map50_bbox, map_mask, map50_mask = results
|
||||
except Exception as e:
|
||||
LOGGER.info(f"pycocotools unable to run: {e}")
|
||||
|
||||
# Return results
|
||||
model.float() # for training
|
||||
if not training:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask
|
||||
return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t
|
||||
|
||||
|
||||
def parse_opt():
|
||||
"""Parses command line arguments for configuring YOLOv5 options like dataset path, weights, batch size, and
|
||||
inference settings.
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)")
|
||||
parser.add_argument("--batch-size", type=int, default=32, help="batch size")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
|
||||
parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold")
|
||||
parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
|
||||
parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
|
||||
parser.add_argument("--task", default="val", help="train, val, test, speed or study")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset")
|
||||
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||||
parser.add_argument("--verbose", action="store_true", help="report mAP by class")
|
||||
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||||
parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt")
|
||||
parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
|
||||
parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file")
|
||||
parser.add_argument("--project", default=ROOT / "runs/val-seg", help="save results to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
opt = parser.parse_args()
|
||||
opt.data = check_yaml(opt.data) # check YAML
|
||||
# opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
opt.save_txt |= opt.save_hybrid
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
"""Executes YOLOv5 tasks including training, validation, testing, speed, and study with configurable options."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
|
||||
if opt.task in ("train", "val", "test"): # run normally
|
||||
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
|
||||
LOGGER.warning(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
|
||||
if opt.save_hybrid:
|
||||
LOGGER.warning("WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone")
|
||||
run(**vars(opt))
|
||||
|
||||
else:
|
||||
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
|
||||
opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results
|
||||
if opt.task == "speed": # speed benchmarks
|
||||
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
|
||||
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
|
||||
for opt.weights in weights:
|
||||
run(**vars(opt), plots=False)
|
||||
|
||||
elif opt.task == "study": # speed vs mAP benchmarks
|
||||
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
|
||||
for opt.weights in weights:
|
||||
f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to
|
||||
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
|
||||
for opt.imgsz in x: # img-size
|
||||
LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
|
||||
r, _, t = run(**vars(opt), plots=False)
|
||||
y.append(r + t) # results and times
|
||||
np.savetxt(f, y, fmt="%10.4g") # save
|
||||
subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
|
||||
plot_val_study(x=x) # plot
|
||||
else:
|
||||
raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
987
third_party/yolov5/train.py
vendored
Normal file
987
third_party/yolov5/train.py
vendored
Normal file
@ -0,0 +1,987 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""
|
||||
Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release.
|
||||
|
||||
Usage - Single-GPU training:
|
||||
$ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)
|
||||
$ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
|
||||
|
||||
Usage - Multi-GPU DDP training:
|
||||
$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3
|
||||
|
||||
Models: https://github.com/ultralytics/yolov5/tree/master/models
|
||||
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
|
||||
Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
|
||||
try:
|
||||
import comet_ml # must be imported before torch (if installed)
|
||||
except ImportError:
|
||||
comet_ml = None
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import yaml
|
||||
from torch.optim import lr_scheduler
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from ultralytics.utils.patches import torch_load
|
||||
|
||||
import val as validate # for end-of-epoch mAP
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import Model
|
||||
from utils.autoanchor import check_anchors
|
||||
from utils.autobatch import check_train_batch_size
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.dataloaders import create_dataloader
|
||||
from utils.downloads import attempt_download, is_url
|
||||
from utils.general import (
|
||||
LOGGER,
|
||||
TQDM_BAR_FORMAT,
|
||||
check_amp,
|
||||
check_dataset,
|
||||
check_file,
|
||||
check_git_info,
|
||||
check_git_status,
|
||||
check_img_size,
|
||||
check_requirements,
|
||||
check_suffix,
|
||||
check_yaml,
|
||||
colorstr,
|
||||
get_latest_run,
|
||||
increment_path,
|
||||
init_seeds,
|
||||
intersect_dicts,
|
||||
labels_to_class_weights,
|
||||
labels_to_image_weights,
|
||||
methods,
|
||||
one_cycle,
|
||||
print_args,
|
||||
print_mutation,
|
||||
strip_optimizer,
|
||||
yaml_save,
|
||||
)
|
||||
from utils.loggers import LOGGERS, Loggers
|
||||
from utils.loggers.comet.comet_utils import check_comet_resume
|
||||
from utils.loss import ComputeLoss
|
||||
from utils.metrics import fitness
|
||||
from utils.plots import plot_evolve
|
||||
from utils.torch_utils import (
|
||||
EarlyStopping,
|
||||
ModelEMA,
|
||||
de_parallel,
|
||||
select_device,
|
||||
smart_DDP,
|
||||
smart_optimizer,
|
||||
smart_resume,
|
||||
torch_distributed_zero_first,
|
||||
)
|
||||
|
||||
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
|
||||
RANK = int(os.getenv("RANK", -1))
|
||||
WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
|
||||
GIT_INFO = check_git_info()
|
||||
|
||||
|
||||
def train(hyp, opt, device, callbacks):
|
||||
"""Train a YOLOv5 model on a custom dataset using specified hyperparameters, options, and device, managing datasets,
|
||||
model architecture, loss computation, and optimizer steps.
|
||||
|
||||
Args:
|
||||
hyp (str | dict): Path to the hyperparameters YAML file or a dictionary of hyperparameters.
|
||||
opt (argparse.Namespace): Parsed command-line arguments containing training options.
|
||||
device (torch.device): Device on which training occurs, e.g., 'cuda' or 'cpu'.
|
||||
callbacks (Callbacks): Callback functions for various training events.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
Single-GPU training:
|
||||
```bash
|
||||
$ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)
|
||||
$ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
|
||||
```
|
||||
|
||||
Multi-GPU DDP training:
|
||||
```bash
|
||||
$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights
|
||||
yolov5s.pt --img 640 --device 0,1,2,3
|
||||
```
|
||||
|
||||
For more usage details, refer to:
|
||||
- Models: https://github.com/ultralytics/yolov5/tree/master/models
|
||||
- Datasets: https://github.com/ultralytics/yolov5/tree/master/data
|
||||
- Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
|
||||
|
||||
Notes:
|
||||
Models and datasets download automatically from the latest YOLOv5 release.
|
||||
"""
|
||||
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = (
|
||||
Path(opt.save_dir),
|
||||
opt.epochs,
|
||||
opt.batch_size,
|
||||
opt.weights,
|
||||
opt.single_cls,
|
||||
opt.evolve,
|
||||
opt.data,
|
||||
opt.cfg,
|
||||
opt.resume,
|
||||
opt.noval,
|
||||
opt.nosave,
|
||||
opt.workers,
|
||||
opt.freeze,
|
||||
)
|
||||
callbacks.run("on_pretrain_routine_start")
|
||||
|
||||
# Directories
|
||||
w = save_dir / "weights" # weights dir
|
||||
(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
|
||||
last, best = w / "last.pt", w / "best.pt"
|
||||
|
||||
# Hyperparameters
|
||||
if isinstance(hyp, str):
|
||||
with open(hyp, errors="ignore") as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items()))
|
||||
opt.hyp = hyp.copy() # for saving hyps to checkpoints
|
||||
|
||||
# Save run settings
|
||||
if not evolve:
|
||||
yaml_save(save_dir / "hyp.yaml", hyp)
|
||||
yaml_save(save_dir / "opt.yaml", vars(opt))
|
||||
|
||||
# Loggers
|
||||
data_dict = None
|
||||
if RANK in {-1, 0}:
|
||||
include_loggers = list(LOGGERS)
|
||||
if getattr(opt, "ndjson_console", False):
|
||||
include_loggers.append("ndjson_console")
|
||||
if getattr(opt, "ndjson_file", False):
|
||||
include_loggers.append("ndjson_file")
|
||||
|
||||
loggers = Loggers(
|
||||
save_dir=save_dir,
|
||||
weights=weights,
|
||||
opt=opt,
|
||||
hyp=hyp,
|
||||
logger=LOGGER,
|
||||
include=tuple(include_loggers),
|
||||
)
|
||||
|
||||
# Register actions
|
||||
for k in methods(loggers):
|
||||
callbacks.register_action(k, callback=getattr(loggers, k))
|
||||
|
||||
# Process custom dataset artifact link
|
||||
data_dict = loggers.remote_dataset
|
||||
if resume: # If resuming runs from remote artifact
|
||||
weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
|
||||
|
||||
# Config
|
||||
plots = not evolve and not opt.noplots # create plots
|
||||
cuda = device.type != "cpu"
|
||||
init_seeds(opt.seed + 1 + RANK, deterministic=True)
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
data_dict = data_dict or check_dataset(data) # check if None
|
||||
train_path, val_path = data_dict["train"], data_dict["val"]
|
||||
nc = 1 if single_cls else int(data_dict["nc"]) # number of classes
|
||||
names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names
|
||||
is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset
|
||||
|
||||
# Model
|
||||
check_suffix(weights, ".pt") # check weights
|
||||
pretrained = weights.endswith(".pt")
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
weights = attempt_download(weights) # download if not found locally
|
||||
ckpt = torch_load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak
|
||||
model = Model(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create
|
||||
exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys
|
||||
csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report
|
||||
else:
|
||||
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create
|
||||
amp = check_amp(model) # check AMP
|
||||
|
||||
# Freeze
|
||||
freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = True # train all layers
|
||||
# v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
|
||||
if any(x in k for x in freeze):
|
||||
LOGGER.info(f"freezing {k}")
|
||||
v.requires_grad = False
|
||||
|
||||
# Image size
|
||||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||||
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
|
||||
|
||||
# Batch size
|
||||
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
|
||||
batch_size = check_train_batch_size(model, imgsz, amp)
|
||||
loggers.on_params_update({"batch_size": batch_size})
|
||||
|
||||
# Optimizer
|
||||
nbs = 64 # nominal batch size
|
||||
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
|
||||
hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay
|
||||
optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"])
|
||||
|
||||
# Scheduler
|
||||
if opt.cos_lr:
|
||||
lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf']
|
||||
else:
|
||||
|
||||
def lf(x):
|
||||
"""Linear learning rate scheduler function with decay calculated by epoch proportion."""
|
||||
return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear
|
||||
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if RANK in {-1, 0} else None
|
||||
|
||||
# Resume
|
||||
best_fitness, start_epoch = 0.0, 0
|
||||
if pretrained:
|
||||
if resume:
|
||||
best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
|
||||
del ckpt, csd
|
||||
|
||||
# DP mode
|
||||
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
|
||||
LOGGER.warning(
|
||||
"WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n"
|
||||
"See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started."
|
||||
)
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# SyncBatchNorm
|
||||
if opt.sync_bn and cuda and RANK != -1:
|
||||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
|
||||
LOGGER.info("Using SyncBatchNorm()")
|
||||
|
||||
# Trainloader
|
||||
train_loader, dataset = create_dataloader(
|
||||
train_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
augment=True,
|
||||
cache=None if opt.cache == "val" else opt.cache,
|
||||
rect=opt.rect,
|
||||
rank=LOCAL_RANK,
|
||||
workers=workers,
|
||||
image_weights=opt.image_weights,
|
||||
quad=opt.quad,
|
||||
prefix=colorstr("train: "),
|
||||
shuffle=True,
|
||||
seed=opt.seed,
|
||||
)
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
mlc = int(labels[:, 0].max()) # max label class
|
||||
assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}"
|
||||
|
||||
# Process 0
|
||||
if RANK in {-1, 0}:
|
||||
val_loader = create_dataloader(
|
||||
val_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE * 2,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
cache=None if noval else opt.cache,
|
||||
rect=True,
|
||||
rank=-1,
|
||||
workers=workers * 2,
|
||||
pad=0.5,
|
||||
prefix=colorstr("val: "),
|
||||
)[0]
|
||||
|
||||
if not resume:
|
||||
if not opt.noautoanchor:
|
||||
check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor
|
||||
model.half().float() # pre-reduce anchor precision
|
||||
|
||||
callbacks.run("on_pretrain_routine_end", labels, names)
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = smart_DDP(model)
|
||||
|
||||
# Model attributes
|
||||
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
|
||||
hyp["box"] *= 3 / nl # scale to layers
|
||||
hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers
|
||||
hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
|
||||
hyp["label_smoothing"] = opt.label_smoothing
|
||||
model.nc = nc # attach number of classes to model
|
||||
model.hyp = hyp # attach hyperparameters to model
|
||||
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
|
||||
model.names = names
|
||||
|
||||
# Start training
|
||||
t0 = time.time()
|
||||
nb = len(train_loader) # number of batches
|
||||
nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
|
||||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||||
last_opt_step = -1
|
||||
maps = np.zeros(nc) # mAP per class
|
||||
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||||
scheduler.last_epoch = start_epoch - 1 # do not move
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=amp)
|
||||
stopper, stop = EarlyStopping(patience=opt.patience), False
|
||||
compute_loss = ComputeLoss(model) # init loss class
|
||||
callbacks.run("on_train_start")
|
||||
LOGGER.info(
|
||||
f"Image sizes {imgsz} train, {imgsz} val\n"
|
||||
f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n"
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f"Starting training for {epochs} epochs..."
|
||||
)
|
||||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||||
callbacks.run("on_train_epoch_start")
|
||||
model.train()
|
||||
|
||||
# Update image weights (optional, single-GPU only)
|
||||
if opt.image_weights:
|
||||
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
|
||||
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
|
||||
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
|
||||
|
||||
# Update mosaic border (optional)
|
||||
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
|
||||
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
|
||||
|
||||
mloss = torch.zeros(3, device=device) # mean losses
|
||||
if RANK != -1:
|
||||
train_loader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(train_loader)
|
||||
LOGGER.info(("\n" + "%11s" * 7) % ("Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size"))
|
||||
if RANK in {-1, 0}:
|
||||
pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
|
||||
callbacks.run("on_train_batch_start")
|
||||
ni = i + nb * epoch # number integrated batches (since train start)
|
||||
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
|
||||
|
||||
# Warmup
|
||||
if ni <= nw:
|
||||
xi = [0, nw] # x interp
|
||||
# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
|
||||
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
|
||||
for j, x in enumerate(optimizer.param_groups):
|
||||
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
|
||||
x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)])
|
||||
if "momentum" in x:
|
||||
x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]])
|
||||
|
||||
# Multi-scale
|
||||
if opt.multi_scale:
|
||||
sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size
|
||||
sf = sz / max(imgs.shape[2:]) # scale factor
|
||||
if sf != 1:
|
||||
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
|
||||
imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
|
||||
|
||||
# Forward
|
||||
with torch.cuda.amp.autocast(amp):
|
||||
pred = model(imgs) # forward
|
||||
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
|
||||
if RANK != -1:
|
||||
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
|
||||
if opt.quad:
|
||||
loss *= 4.0
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
|
||||
if ni - last_opt_step >= accumulate:
|
||||
scaler.unscale_(optimizer) # unscale gradients
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
|
||||
scaler.step(optimizer) # optimizer.step
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
last_opt_step = ni
|
||||
|
||||
# Log
|
||||
if RANK in {-1, 0}:
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
|
||||
pbar.set_description(
|
||||
("%11s" * 2 + "%11.4g" * 5)
|
||||
% (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||||
)
|
||||
callbacks.run("on_train_batch_end", model, ni, imgs, targets, paths, list(mloss))
|
||||
if callbacks.stop_training:
|
||||
return
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
|
||||
# Scheduler
|
||||
lr = [x["lr"] for x in optimizer.param_groups] # for loggers
|
||||
scheduler.step()
|
||||
|
||||
if RANK in {-1, 0}:
|
||||
# mAP
|
||||
callbacks.run("on_train_epoch_end", epoch=epoch)
|
||||
ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"])
|
||||
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
|
||||
if not noval or final_epoch: # Calculate mAP
|
||||
results, maps, _ = validate.run(
|
||||
data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
half=amp,
|
||||
model=ema.ema,
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
plots=False,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss,
|
||||
)
|
||||
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||||
stop = stopper(epoch=epoch, fitness=fi) # early stop check
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
log_vals = list(mloss) + list(results) + lr
|
||||
callbacks.run("on_fit_epoch_end", log_vals, epoch, best_fitness, fi)
|
||||
|
||||
# Save model
|
||||
if (not nosave) or (final_epoch and not evolve): # if save
|
||||
ckpt = {
|
||||
"epoch": epoch,
|
||||
"best_fitness": best_fitness,
|
||||
"model": deepcopy(de_parallel(model)).half(),
|
||||
"ema": deepcopy(ema.ema).half(),
|
||||
"updates": ema.updates,
|
||||
"optimizer": optimizer.state_dict(),
|
||||
"opt": vars(opt),
|
||||
"git": GIT_INFO, # {remote, branch, commit} if a git repo
|
||||
"date": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fi:
|
||||
torch.save(ckpt, best)
|
||||
if opt.save_period > 0 and epoch % opt.save_period == 0:
|
||||
torch.save(ckpt, w / f"epoch{epoch}.pt")
|
||||
del ckpt
|
||||
callbacks.run("on_model_save", last, epoch, final_epoch, best_fitness, fi)
|
||||
|
||||
# EarlyStopping
|
||||
if RANK != -1: # if DDP training
|
||||
broadcast_list = [stop if RANK == 0 else None]
|
||||
dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
|
||||
if RANK != 0:
|
||||
stop = broadcast_list[0]
|
||||
if stop:
|
||||
break # must break all DDP ranks
|
||||
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training -----------------------------------------------------------------------------------------------------
|
||||
if RANK in {-1, 0}:
|
||||
LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.")
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if f is best:
|
||||
LOGGER.info(f"\nValidating {f}...")
|
||||
results, _, _ = validate.run(
|
||||
data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=attempt_load(f, device).half(),
|
||||
iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
save_json=is_coco,
|
||||
verbose=True,
|
||||
plots=plots,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss,
|
||||
) # val best model with plots
|
||||
if is_coco:
|
||||
callbacks.run("on_fit_epoch_end", list(mloss) + list(results) + lr, epoch, best_fitness, fi)
|
||||
|
||||
callbacks.run("on_train_end", last, best, epoch, results)
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
|
||||
|
||||
def parse_opt(known=False):
|
||||
"""Parse command-line arguments for YOLOv5 training, validation, and testing.
|
||||
|
||||
Args:
|
||||
known (bool, optional): If True, parses known arguments, ignoring the unknown. Defaults to False.
|
||||
|
||||
Returns:
|
||||
(argparse.Namespace): Parsed command-line arguments containing options for YOLOv5 execution.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
from ultralytics.yolo import parse_opt
|
||||
opt = parse_opt()
|
||||
print(opt)
|
||||
```
|
||||
|
||||
Links:
|
||||
- Models: https://github.com/ultralytics/yolov5/tree/master/models
|
||||
- Datasets: https://github.com/ultralytics/yolov5/tree/master/data
|
||||
- Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
|
||||
"""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path")
|
||||
parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path")
|
||||
parser.add_argument("--epochs", type=int, default=100, help="total training epochs")
|
||||
parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)")
|
||||
parser.add_argument("--rect", action="store_true", help="rectangular training")
|
||||
parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training")
|
||||
parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
|
||||
parser.add_argument("--noval", action="store_true", help="only validate final epoch")
|
||||
parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor")
|
||||
parser.add_argument("--noplots", action="store_true", help="save no plot files")
|
||||
parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations")
|
||||
parser.add_argument(
|
||||
"--evolve_population", type=str, default=ROOT / "data/hyps", help="location for loading population"
|
||||
)
|
||||
parser.add_argument("--resume_evolve", type=str, default=None, help="resume evolve from last generation")
|
||||
parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
|
||||
parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk")
|
||||
parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%")
|
||||
parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class")
|
||||
parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer")
|
||||
parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--quad", action="store_true", help="quad dataloader")
|
||||
parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler")
|
||||
parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon")
|
||||
parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)")
|
||||
parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2")
|
||||
parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)")
|
||||
parser.add_argument("--seed", type=int, default=0, help="Global training seed")
|
||||
parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
|
||||
|
||||
# Logger arguments
|
||||
parser.add_argument("--entity", default=None, help="Entity")
|
||||
parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='Upload data, "val" option')
|
||||
parser.add_argument("--bbox_interval", type=int, default=-1, help="Set bounding-box image logging interval")
|
||||
parser.add_argument("--artifact_alias", type=str, default="latest", help="Version of dataset artifact to use")
|
||||
|
||||
# NDJSON logging
|
||||
parser.add_argument("--ndjson-console", action="store_true", help="Log ndjson to console")
|
||||
parser.add_argument("--ndjson-file", action="store_true", help="Log ndjson to file")
|
||||
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
def main(opt, callbacks=Callbacks()):
|
||||
"""Runs the main entry point for training or hyperparameter evolution with specified options and optional callbacks.
|
||||
|
||||
Args:
|
||||
opt (argparse.Namespace): The command-line arguments parsed for YOLOv5 training and evolution.
|
||||
callbacks (ultralytics.utils.callbacks.Callbacks, optional): Callback functions for various training stages.
|
||||
Defaults to Callbacks().
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Notes:
|
||||
For detailed usage, refer to:
|
||||
https://github.com/ultralytics/yolov5/tree/master/models
|
||||
"""
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
check_git_status()
|
||||
check_requirements(ROOT / "requirements.txt")
|
||||
|
||||
# Resume (from specified or most recent last.pt)
|
||||
if opt.resume and not check_comet_resume(opt) and not opt.evolve:
|
||||
last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
|
||||
opt_yaml = last.parent.parent / "opt.yaml" # train options yaml
|
||||
opt_data = opt.data # original dataset
|
||||
if opt_yaml.is_file():
|
||||
with open(opt_yaml, errors="ignore") as f:
|
||||
d = yaml.safe_load(f)
|
||||
else:
|
||||
d = torch_load(last, map_location="cpu")["opt"]
|
||||
opt = argparse.Namespace(**d) # replace
|
||||
opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate
|
||||
if is_url(opt_data):
|
||||
opt.data = check_file(opt_data) # avoid HUB resume auth timeout
|
||||
else:
|
||||
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = (
|
||||
check_file(opt.data),
|
||||
check_yaml(opt.cfg),
|
||||
check_yaml(opt.hyp),
|
||||
str(opt.weights),
|
||||
str(opt.project),
|
||||
) # checks
|
||||
assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified"
|
||||
if opt.evolve:
|
||||
if opt.project == str(ROOT / "runs/train"): # if default project name, rename to runs/evolve
|
||||
opt.project = str(ROOT / "runs/evolve")
|
||||
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
|
||||
if opt.name == "cfg":
|
||||
opt.name = Path(opt.cfg).stem # use model.yaml as name
|
||||
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
msg = "is not compatible with YOLOv5 Multi-GPU DDP training"
|
||||
assert not opt.image_weights, f"--image-weights {msg}"
|
||||
assert not opt.evolve, f"--evolve {msg}"
|
||||
assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size"
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device("cuda", LOCAL_RANK)
|
||||
dist.init_process_group(
|
||||
backend="nccl" if dist.is_nccl_available() else "gloo", timeout=timedelta(seconds=10800)
|
||||
)
|
||||
|
||||
# Train
|
||||
if not opt.evolve:
|
||||
train(opt.hyp, opt, device, callbacks)
|
||||
|
||||
# Evolve hyperparameters (optional)
|
||||
else:
|
||||
# Hyperparameter evolution metadata (including this hyperparameter True-False, lower_limit, upper_limit)
|
||||
meta = {
|
||||
"lr0": (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
"lrf": (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
"momentum": (False, 0.6, 0.98), # SGD momentum/Adam beta1
|
||||
"weight_decay": (False, 0.0, 0.001), # optimizer weight decay
|
||||
"warmup_epochs": (False, 0.0, 5.0), # warmup epochs (fractions ok)
|
||||
"warmup_momentum": (False, 0.0, 0.95), # warmup initial momentum
|
||||
"warmup_bias_lr": (False, 0.0, 0.2), # warmup initial bias lr
|
||||
"box": (False, 0.02, 0.2), # box loss gain
|
||||
"cls": (False, 0.2, 4.0), # cls loss gain
|
||||
"cls_pw": (False, 0.5, 2.0), # cls BCELoss positive_weight
|
||||
"obj": (False, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||||
"obj_pw": (False, 0.5, 2.0), # obj BCELoss positive_weight
|
||||
"iou_t": (False, 0.1, 0.7), # IoU training threshold
|
||||
"anchor_t": (False, 2.0, 8.0), # anchor-multiple threshold
|
||||
"anchors": (False, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||||
"fl_gamma": (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||||
"hsv_h": (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
"hsv_s": (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
"hsv_v": (True, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
"degrees": (True, 0.0, 45.0), # image rotation (+/- deg)
|
||||
"translate": (True, 0.0, 0.9), # image translation (+/- fraction)
|
||||
"scale": (True, 0.0, 0.9), # image scale (+/- gain)
|
||||
"shear": (True, 0.0, 10.0), # image shear (+/- deg)
|
||||
"perspective": (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
"flipud": (True, 0.0, 1.0), # image flip up-down (probability)
|
||||
"fliplr": (True, 0.0, 1.0), # image flip left-right (probability)
|
||||
"mosaic": (True, 0.0, 1.0), # image mosaic (probability)
|
||||
"mixup": (True, 0.0, 1.0), # image mixup (probability)
|
||||
"copy_paste": (True, 0.0, 1.0), # segment copy-paste (probability)
|
||||
}
|
||||
|
||||
# GA configs
|
||||
pop_size = 50
|
||||
mutation_rate_min = 0.01
|
||||
mutation_rate_max = 0.5
|
||||
crossover_rate_min = 0.5
|
||||
crossover_rate_max = 1
|
||||
min_elite_size = 2
|
||||
max_elite_size = 5
|
||||
tournament_size_min = 2
|
||||
tournament_size_max = 10
|
||||
|
||||
with open(opt.hyp, errors="ignore") as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
if "anchors" not in hyp: # anchors commented in hyp.yaml
|
||||
hyp["anchors"] = 3
|
||||
if opt.noautoanchor:
|
||||
del hyp["anchors"], meta["anchors"]
|
||||
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
|
||||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||||
evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv"
|
||||
if opt.bucket:
|
||||
# download evolve.csv if exists
|
||||
subprocess.run(
|
||||
[
|
||||
"gsutil",
|
||||
"cp",
|
||||
f"gs://{opt.bucket}/evolve.csv",
|
||||
str(evolve_csv),
|
||||
]
|
||||
)
|
||||
|
||||
# Delete the items in meta dictionary whose first value is False
|
||||
del_ = [item for item, value_ in meta.items() if value_[0] is False]
|
||||
hyp_GA = hyp.copy() # Make a copy of hyp dictionary
|
||||
for item in del_:
|
||||
del meta[item] # Remove the item from meta dictionary
|
||||
del hyp_GA[item] # Remove the item from hyp_GA dictionary
|
||||
|
||||
# Set lower_limit and upper_limit arrays to hold the search space boundaries
|
||||
lower_limit = np.array([meta[k][1] for k in hyp_GA.keys()])
|
||||
upper_limit = np.array([meta[k][2] for k in hyp_GA.keys()])
|
||||
|
||||
# Create gene_ranges list to hold the range of values for each gene in the population
|
||||
gene_ranges = [(lower_limit[i], upper_limit[i]) for i in range(len(upper_limit))]
|
||||
|
||||
# Initialize the population with initial_values or random values
|
||||
initial_values = []
|
||||
|
||||
# If resuming evolution from a previous checkpoint
|
||||
if opt.resume_evolve is not None:
|
||||
assert os.path.isfile(ROOT / opt.resume_evolve), "evolve population path is wrong!"
|
||||
with open(ROOT / opt.resume_evolve, errors="ignore") as f:
|
||||
evolve_population = yaml.safe_load(f)
|
||||
for value in evolve_population.values():
|
||||
value = np.array([value[k] for k in hyp_GA.keys()])
|
||||
initial_values.append(list(value))
|
||||
|
||||
# If not resuming from a previous checkpoint, generate initial values from .yaml files in opt.evolve_population
|
||||
else:
|
||||
yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith(".yaml")]
|
||||
for file_name in yaml_files:
|
||||
with open(os.path.join(opt.evolve_population, file_name)) as yaml_file:
|
||||
value = yaml.safe_load(yaml_file)
|
||||
value = np.array([value[k] for k in hyp_GA.keys()])
|
||||
initial_values.append(list(value))
|
||||
|
||||
# Generate random values within the search space for the rest of the population
|
||||
if initial_values is None:
|
||||
population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size)]
|
||||
elif pop_size > 1:
|
||||
population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size - len(initial_values))]
|
||||
for initial_value in initial_values:
|
||||
population = [initial_value, *population]
|
||||
|
||||
# Run the genetic algorithm for a fixed number of generations
|
||||
list_keys = list(hyp_GA.keys())
|
||||
for generation in range(opt.evolve):
|
||||
if generation >= 1:
|
||||
save_dict = {}
|
||||
for i in range(len(population)):
|
||||
little_dict = {list_keys[j]: float(population[i][j]) for j in range(len(population[i]))}
|
||||
save_dict[f"gen{generation!s}number{i!s}"] = little_dict
|
||||
|
||||
with open(save_dir / "evolve_population.yaml", "w") as outfile:
|
||||
yaml.dump(save_dict, outfile, default_flow_style=False)
|
||||
|
||||
# Adaptive elite size
|
||||
elite_size = min_elite_size + int((max_elite_size - min_elite_size) * (generation / opt.evolve))
|
||||
# Evaluate the fitness of each individual in the population
|
||||
fitness_scores = []
|
||||
for individual in population:
|
||||
for key, value in zip(hyp_GA.keys(), individual):
|
||||
hyp_GA[key] = value
|
||||
hyp.update(hyp_GA)
|
||||
results = train(hyp.copy(), opt, device, callbacks)
|
||||
callbacks = Callbacks()
|
||||
# Write mutation results
|
||||
keys = (
|
||||
"metrics/precision",
|
||||
"metrics/recall",
|
||||
"metrics/mAP_0.5",
|
||||
"metrics/mAP_0.5:0.95",
|
||||
"val/box_loss",
|
||||
"val/obj_loss",
|
||||
"val/cls_loss",
|
||||
)
|
||||
print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)
|
||||
fitness_scores.append(results[2])
|
||||
|
||||
# Select the fittest individuals for reproduction using adaptive tournament selection
|
||||
selected_indices = []
|
||||
for _ in range(pop_size - elite_size):
|
||||
# Adaptive tournament size
|
||||
tournament_size = max(
|
||||
max(2, tournament_size_min),
|
||||
int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10))),
|
||||
)
|
||||
# Perform tournament selection to choose the best individual
|
||||
tournament_indices = random.sample(range(pop_size), tournament_size)
|
||||
tournament_fitness = [fitness_scores[j] for j in tournament_indices]
|
||||
winner_index = tournament_indices[tournament_fitness.index(max(tournament_fitness))]
|
||||
selected_indices.append(winner_index)
|
||||
|
||||
# Add the elite individuals to the selected indices
|
||||
elite_indices = [i for i in range(pop_size) if fitness_scores[i] in sorted(fitness_scores)[-elite_size:]]
|
||||
selected_indices.extend(elite_indices)
|
||||
# Create the next generation through crossover and mutation
|
||||
next_generation = []
|
||||
for _ in range(pop_size):
|
||||
parent1_index = selected_indices[random.randint(0, pop_size - 1)]
|
||||
parent2_index = selected_indices[random.randint(0, pop_size - 1)]
|
||||
# Adaptive crossover rate
|
||||
crossover_rate = max(
|
||||
crossover_rate_min, min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve))
|
||||
)
|
||||
if random.uniform(0, 1) < crossover_rate:
|
||||
crossover_point = random.randint(1, len(hyp_GA) - 1)
|
||||
child = population[parent1_index][:crossover_point] + population[parent2_index][crossover_point:]
|
||||
else:
|
||||
child = population[parent1_index]
|
||||
# Adaptive mutation rate
|
||||
mutation_rate = max(
|
||||
mutation_rate_min, min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve))
|
||||
)
|
||||
for j in range(len(hyp_GA)):
|
||||
if random.uniform(0, 1) < mutation_rate:
|
||||
child[j] += random.uniform(-0.1, 0.1)
|
||||
child[j] = min(max(child[j], gene_ranges[j][0]), gene_ranges[j][1])
|
||||
next_generation.append(child)
|
||||
# Replace the old population with the new generation
|
||||
population = next_generation
|
||||
# Print the best solution found
|
||||
best_index = fitness_scores.index(max(fitness_scores))
|
||||
best_individual = population[best_index]
|
||||
print("Best solution found:", best_individual)
|
||||
# Plot results
|
||||
plot_evolve(evolve_csv)
|
||||
LOGGER.info(
|
||||
f"Hyperparameter evolution finished {opt.evolve} generations\n"
|
||||
f"Results saved to {colorstr('bold', save_dir)}\n"
|
||||
f"Usage example: $ python train.py --hyp {evolve_yaml}"
|
||||
)
|
||||
|
||||
|
||||
def generate_individual(input_ranges, individual_length):
|
||||
"""Generate an individual with random hyperparameters within specified ranges.
|
||||
|
||||
Args:
|
||||
input_ranges (list[tuple[float, float]]): List of tuples where each tuple contains the lower and upper bounds
|
||||
for the corresponding gene (hyperparameter).
|
||||
individual_length (int): The number of genes (hyperparameters) in the individual.
|
||||
|
||||
Returns:
|
||||
list[float]: A list representing a generated individual with random gene values within the specified ranges.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
input_ranges = [(0.01, 0.1), (0.1, 1.0), (0.9, 2.0)]
|
||||
individual_length = 3
|
||||
individual = generate_individual(input_ranges, individual_length)
|
||||
print(individual) # Output: [0.035, 0.678, 1.456] (example output)
|
||||
```
|
||||
|
||||
Notes:
|
||||
The individual returned will have a length equal to `individual_length`, with each gene value being a floating-point
|
||||
number within its specified range in `input_ranges`.
|
||||
"""
|
||||
individual = []
|
||||
for i in range(individual_length):
|
||||
lower_bound, upper_bound = input_ranges[i]
|
||||
individual.append(random.uniform(lower_bound, upper_bound))
|
||||
return individual
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
"""Execute YOLOv5 training with specified options, allowing optional overrides through keyword arguments.
|
||||
|
||||
Args:
|
||||
weights (str, optional): Path to initial weights. Defaults to ROOT / 'yolov5s.pt'.
|
||||
cfg (str, optional): Path to model YAML configuration. Defaults to an empty string.
|
||||
data (str, optional): Path to dataset YAML configuration. Defaults to ROOT / 'data/coco128.yaml'.
|
||||
hyp (str, optional): Path to hyperparameters YAML configuration. Defaults to ROOT /
|
||||
'data/hyps/hyp.scratch-low.yaml'.
|
||||
epochs (int, optional): Total number of training epochs. Defaults to 100.
|
||||
batch_size (int, optional): Total batch size for all GPUs. Use -1 for automatic batch size determination.
|
||||
Defaults to 16.
|
||||
imgsz (int, optional): Image size (pixels) for training and validation. Defaults to 640.
|
||||
rect (bool, optional): Use rectangular training. Defaults to False.
|
||||
resume (bool | str, optional): Resume most recent training with an optional path. Defaults to False.
|
||||
nosave (bool, optional): Only save the final checkpoint. Defaults to False.
|
||||
noval (bool, optional): Only validate at the final epoch. Defaults to False.
|
||||
noautoanchor (bool, optional): Disable AutoAnchor. Defaults to False.
|
||||
noplots (bool, optional): Do not save plot files. Defaults to False.
|
||||
evolve (int, optional): Evolve hyperparameters for a specified number of generations. Use 300 if provided
|
||||
without a value.
|
||||
evolve_population (str, optional): Directory for loading population during evolution. Defaults to ROOT / 'data/
|
||||
hyps'.
|
||||
resume_evolve (str, optional): Resume hyperparameter evolution from the last generation. Defaults to None.
|
||||
bucket (str, optional): gsutil bucket for saving checkpoints. Defaults to an empty string.
|
||||
cache (str, optional): Cache image data in 'ram' or 'disk'. Defaults to None.
|
||||
image_weights (bool, optional): Use weighted image selection for training. Defaults to False.
|
||||
device (str, optional): CUDA device identifier, e.g., '0', '0,1,2,3', or 'cpu'. Defaults to an empty string.
|
||||
multi_scale (bool, optional): Use multi-scale training, varying image size by ±50%. Defaults to False.
|
||||
single_cls (bool, optional): Train with multi-class data as single-class. Defaults to False.
|
||||
optimizer (str, optional): Optimizer type, choices are ['SGD', 'Adam', 'AdamW']. Defaults to 'SGD'.
|
||||
sync_bn (bool, optional): Use synchronized BatchNorm, only available in DDP mode. Defaults to False.
|
||||
workers (int, optional): Maximum dataloader workers per rank in DDP mode. Defaults to 8.
|
||||
project (str, optional): Directory for saving training runs. Defaults to ROOT / 'runs/train'.
|
||||
name (str, optional): Name for saving the training run. Defaults to 'exp'.
|
||||
exist_ok (bool, optional): Allow existing project/name without incrementing. Defaults to False.
|
||||
quad (bool, optional): Use quad dataloader. Defaults to False.
|
||||
cos_lr (bool, optional): Use cosine learning rate scheduler. Defaults to False.
|
||||
label_smoothing (float, optional): Label smoothing epsilon value. Defaults to 0.0.
|
||||
patience (int, optional): Patience for early stopping, measured in epochs without improvement. Defaults to 100.
|
||||
freeze (list, optional): Layers to freeze, e.g., backbone=10, first 3 layers = [0, 1, 2]. Defaults to [0].
|
||||
save_period (int, optional): Frequency in epochs to save checkpoints. Disabled if < 1. Defaults to -1.
|
||||
seed (int, optional): Global training random seed. Defaults to 0.
|
||||
local_rank (int, optional): Automatic DDP Multi-GPU argument. Do not modify. Defaults to -1.
|
||||
|
||||
Returns:
|
||||
None: The function initiates YOLOv5 training or hyperparameter evolution based on the provided options.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
import train
|
||||
train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
|
||||
```
|
||||
|
||||
Notes:
|
||||
- Models: https://github.com/ultralytics/yolov5/tree/master/models
|
||||
- Datasets: https://github.com/ultralytics/yolov5/tree/master/data
|
||||
- Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
|
||||
"""
|
||||
opt = parse_opt(True)
|
||||
for k, v in kwargs.items():
|
||||
setattr(opt, k, v)
|
||||
main(opt)
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
607
third_party/yolov5/tutorial.ipynb
vendored
Normal file
607
third_party/yolov5/tutorial.ipynb
vendored
Normal file
@ -0,0 +1,607 @@
|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"name": "YOLOv5 Tutorial",
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3"
|
||||
},
|
||||
"accelerator": "GPU"
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "t6MPjfT5NrKQ"
|
||||
},
|
||||
"source": [
|
||||
"<div align=\"center\">\n",
|
||||
" <a href=\"https://ultralytics.com/yolo\" target=\"_blank\">\n",
|
||||
" <img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\">\n",
|
||||
" </a>\n",
|
||||
"\n",
|
||||
" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
|
||||
"\n",
|
||||
" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
|
||||
" <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n",
|
||||
" <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
|
||||
" <a href=\"https://www.kaggle.com/models/ultralytics/yolo11\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
|
||||
"\n",
|
||||
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
|
||||
" <a href=\"https://community.ultralytics.com\"><img alt=\"Ultralytics Forums\" src=\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\"></a>\n",
|
||||
" <a href=\"https://reddit.com/r/ultralytics\"><img alt=\"Ultralytics Reddit\" src=\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue\"></a>\n",
|
||||
"</div>\n",
|
||||
"\n",
|
||||
"This **Ultralytics YOLOv5 Colab Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\n",
|
||||
"\n",
|
||||
"Ultralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\n",
|
||||
"\n",
|
||||
"Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\n",
|
||||
"\n",
|
||||
"Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n",
|
||||
"\n",
|
||||
"<br>\n",
|
||||
"<div>\n",
|
||||
" <a href=\"https://www.youtube.com/watch?v=ZN3nRZT7b24\" target=\"_blank\">\n",
|
||||
" <img src=\"https://img.youtube.com/vi/ZN3nRZT7b24/maxresdefault.jpg\" alt=\"Ultralytics Video\" width=\"640\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);\">\n",
|
||||
" </a>\n",
|
||||
"\n",
|
||||
" <p style=\"font-size: 16px; font-family: Arial, sans-serif; color: #555;\">\n",
|
||||
" <strong>Watch: </strong> How to Train\n",
|
||||
" <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics</a>\n",
|
||||
" <a href=\"https://docs.ultralytics.com/models/yolo11/\">YOLO11</a> Model on Custom Dataset using Google Colab Notebook 🚀\n",
|
||||
" </p>\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "7mGmQbAO5pQb"
|
||||
},
|
||||
"source": [
|
||||
"# Setup\n",
|
||||
"\n",
|
||||
"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "wbvMlHd_QwMG",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "e8225db4-e61d-4640-8b1f-8bfce3331cea"
|
||||
},
|
||||
"source": [
|
||||
"!git clone https://github.com/ultralytics/yolov5 # clone\n",
|
||||
"%cd yolov5\n",
|
||||
"%pip install -qr requirements.txt comet_ml # install\n",
|
||||
"\n",
|
||||
"import torch\n",
|
||||
"import utils\n",
|
||||
"display = utils.notebook_init() # checks"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stderr",
|
||||
"text": [
|
||||
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "4JnkELT0cIJg"
|
||||
},
|
||||
"source": [
|
||||
"# 1. Detect\n",
|
||||
"\n",
|
||||
"`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n",
|
||||
"\n",
|
||||
"```shell\n",
|
||||
"python detect.py --source 0 # webcam\n",
|
||||
" img.jpg # image\n",
|
||||
" vid.mp4 # video\n",
|
||||
" screen # screenshot\n",
|
||||
" path/ # directory\n",
|
||||
" 'path/*.jpg' # glob\n",
|
||||
" 'https://youtu.be/LNwODJXcvt4' # YouTube\n",
|
||||
" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "zR9ZbuQCH7FX",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "284ef04b-1596-412f-88f6-948828dd2b49"
|
||||
},
|
||||
"source": [
|
||||
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
|
||||
"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
|
||||
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
|
||||
"\n",
|
||||
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
|
||||
"100% 14.1M/14.1M [00:00<00:00, 24.5MB/s]\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
|
||||
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 41.5ms\n",
|
||||
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 60.0ms\n",
|
||||
"Speed: 0.5ms pre-process, 50.8ms inference, 37.7ms NMS per image at shape (1, 3, 640, 640)\n",
|
||||
"Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "hkAzDWJ7cWTr"
|
||||
},
|
||||
"source": [
|
||||
" \n",
|
||||
"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\" width=\"600\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "0eq1SMWl6Sfn"
|
||||
},
|
||||
"source": [
|
||||
"# 2. Validate\n",
|
||||
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "WQPtK1QYVaD_",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426"
|
||||
},
|
||||
"source": [
|
||||
"# Download COCO val\n",
|
||||
"torch.hub.download_url_to_file('https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
|
||||
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stderr",
|
||||
"text": [
|
||||
"100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "X58w8JLpMnjH",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d"
|
||||
},
|
||||
"source": [
|
||||
"# Validate YOLOv5s on COCO val\n",
|
||||
"!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
|
||||
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
|
||||
" Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\n",
|
||||
" all 5000 36335 0.671 0.519 0.566 0.371\n",
|
||||
"Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n",
|
||||
"\n",
|
||||
"Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
|
||||
"loading annotations into memory...\n",
|
||||
"Done (t=0.43s)\n",
|
||||
"creating index...\n",
|
||||
"index created!\n",
|
||||
"Loading and preparing results...\n",
|
||||
"DONE (t=5.32s)\n",
|
||||
"creating index...\n",
|
||||
"index created!\n",
|
||||
"Running per image evaluation...\n",
|
||||
"Evaluate annotation type *bbox*\n",
|
||||
"DONE (t=78.89s).\n",
|
||||
"Accumulating evaluation results...\n",
|
||||
"DONE (t=14.51s).\n",
|
||||
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n",
|
||||
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n",
|
||||
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n",
|
||||
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n",
|
||||
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n",
|
||||
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n",
|
||||
"Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ZY2VXXXu74w5"
|
||||
},
|
||||
"source": "# 3. Train\n\n<p align=\"\"><a href=\"https://platform.ultralytics.com\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\nClose the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n<br><br>\n\nTrain a YOLOv5s model on the [COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n\n- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\nautomatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n<br>\n\nA **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic."
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
|
||||
"logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n",
|
||||
"\n",
|
||||
"if logger == 'Comet':\n",
|
||||
" %pip install -q comet_ml\n",
|
||||
" import comet_ml; comet_ml.init()\n",
|
||||
"elif logger == 'ClearML':\n",
|
||||
" %pip install -q clearml\n",
|
||||
" import clearml; clearml.browser_login()\n",
|
||||
"elif logger == 'TensorBoard':\n",
|
||||
" %load_ext tensorboard\n",
|
||||
" %tensorboard --logdir runs/train"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "i3oKtE4g-aNn"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "1NcFxRcFdJ_O",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a"
|
||||
},
|
||||
"source": [
|
||||
"# Train YOLOv5s on COCO128 for 3 epochs\n",
|
||||
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
|
||||
"To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
|
||||
"2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
|
||||
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
|
||||
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
|
||||
"\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
|
||||
"\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
|
||||
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
|
||||
"\n",
|
||||
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
|
||||
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip to coco128.zip...\n",
|
||||
"100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n",
|
||||
"Dataset download success ✅ (0.6s), saved to \u001b[1m/content/datasets\u001b[0m\n",
|
||||
"\n",
|
||||
" from n params module arguments \n",
|
||||
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
|
||||
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
|
||||
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
|
||||
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
|
||||
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
|
||||
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
|
||||
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
|
||||
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
|
||||
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
|
||||
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
|
||||
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
|
||||
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
|
||||
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
|
||||
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
|
||||
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
|
||||
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
|
||||
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
|
||||
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
|
||||
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
|
||||
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
|
||||
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
|
||||
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
|
||||
" 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
|
||||
"Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n",
|
||||
"\n",
|
||||
"Transferred 349/349 items from yolov5s.pt\n",
|
||||
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
|
||||
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
|
||||
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1709.36it/s]\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 107.05it/s]\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
|
||||
"Plotting labels to runs/train/exp/labels.jpg... \n",
|
||||
"Image sizes 640 train, 640 val\n",
|
||||
"Using 2 dataloader workers\n",
|
||||
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
|
||||
"Starting training for 3 epochs...\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||||
" 0/2 3.91G 0.04618 0.07209 0.01703 232 640: 100% 8/8 [00:09<00:00, 1.17s/it]\n",
|
||||
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.01it/s]\n",
|
||||
" all 128 929 0.667 0.602 0.68 0.45\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||||
" 1/2 4.76G 0.04622 0.06891 0.01817 201 640: 100% 8/8 [00:02<00:00, 3.78it/s]\n",
|
||||
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.16it/s]\n",
|
||||
" all 128 929 0.709 0.645 0.722 0.478\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||||
" 2/2 4.76G 0.0436 0.0647 0.01698 227 640: 100% 8/8 [00:01<00:00, 4.19it/s]\n",
|
||||
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.95it/s]\n",
|
||||
" all 128 929 0.761 0.647 0.735 0.49\n",
|
||||
"\n",
|
||||
"3 epochs completed in 0.006 hours.\n",
|
||||
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
|
||||
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n",
|
||||
"\n",
|
||||
"Validating runs/train/exp/weights/best.pt...\n",
|
||||
"Fusing layers... \n",
|
||||
"Model summary: 157 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
|
||||
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:06<00:00, 1.56s/it]\n",
|
||||
" all 128 929 0.759 0.646 0.734 0.49\n",
|
||||
" person 128 254 0.857 0.706 0.805 0.525\n",
|
||||
" bicycle 128 6 0.773 0.577 0.725 0.414\n",
|
||||
" car 128 46 0.664 0.435 0.551 0.24\n",
|
||||
" motorcycle 128 5 0.587 0.8 0.837 0.635\n",
|
||||
" airplane 128 6 1 0.989 0.995 0.715\n",
|
||||
" bus 128 7 0.635 0.714 0.753 0.651\n",
|
||||
" train 128 3 0.686 0.333 0.72 0.504\n",
|
||||
" truck 128 12 0.604 0.333 0.472 0.259\n",
|
||||
" boat 128 6 0.938 0.333 0.449 0.177\n",
|
||||
" traffic light 128 14 0.778 0.255 0.401 0.217\n",
|
||||
" stop sign 128 2 0.826 1 0.995 0.895\n",
|
||||
" bench 128 9 0.711 0.556 0.661 0.313\n",
|
||||
" bird 128 16 0.962 1 0.995 0.642\n",
|
||||
" cat 128 4 0.868 1 0.995 0.754\n",
|
||||
" dog 128 9 1 0.652 0.899 0.651\n",
|
||||
" horse 128 2 0.853 1 0.995 0.622\n",
|
||||
" elephant 128 17 0.909 0.882 0.934 0.698\n",
|
||||
" bear 128 1 0.696 1 0.995 0.995\n",
|
||||
" zebra 128 4 0.855 1 0.995 0.905\n",
|
||||
" giraffe 128 9 0.788 0.828 0.912 0.701\n",
|
||||
" backpack 128 6 0.835 0.5 0.738 0.311\n",
|
||||
" umbrella 128 18 0.785 0.814 0.859 0.48\n",
|
||||
" handbag 128 19 0.759 0.263 0.366 0.205\n",
|
||||
" tie 128 7 0.983 0.714 0.77 0.492\n",
|
||||
" suitcase 128 4 0.656 1 0.945 0.631\n",
|
||||
" frisbee 128 5 0.721 0.8 0.759 0.724\n",
|
||||
" skis 128 1 0.737 1 0.995 0.3\n",
|
||||
" snowboard 128 7 0.829 0.696 0.83 0.537\n",
|
||||
" sports ball 128 6 0.637 0.667 0.602 0.311\n",
|
||||
" kite 128 10 0.636 0.6 0.599 0.226\n",
|
||||
" baseball bat 128 4 0.501 0.25 0.468 0.205\n",
|
||||
" baseball glove 128 7 0.483 0.429 0.465 0.292\n",
|
||||
" skateboard 128 5 0.932 0.6 0.687 0.493\n",
|
||||
" tennis racket 128 7 0.77 0.429 0.547 0.332\n",
|
||||
" bottle 128 18 0.577 0.379 0.554 0.276\n",
|
||||
" wine glass 128 16 0.704 0.875 0.89 0.51\n",
|
||||
" cup 128 36 0.841 0.667 0.837 0.533\n",
|
||||
" fork 128 6 0.992 0.333 0.45 0.315\n",
|
||||
" knife 128 16 0.768 0.688 0.695 0.403\n",
|
||||
" spoon 128 22 0.838 0.47 0.639 0.384\n",
|
||||
" bowl 128 28 0.764 0.58 0.716 0.513\n",
|
||||
" banana 128 1 0.902 1 0.995 0.301\n",
|
||||
" sandwich 128 2 1 0 0.359 0.326\n",
|
||||
" orange 128 4 0.722 0.75 0.912 0.581\n",
|
||||
" broccoli 128 11 0.547 0.364 0.432 0.317\n",
|
||||
" carrot 128 24 0.619 0.625 0.724 0.495\n",
|
||||
" hot dog 128 2 0.409 1 0.828 0.762\n",
|
||||
" pizza 128 5 0.833 0.995 0.962 0.727\n",
|
||||
" donut 128 14 0.631 1 0.96 0.839\n",
|
||||
" cake 128 4 0.87 1 0.995 0.83\n",
|
||||
" chair 128 35 0.583 0.6 0.608 0.317\n",
|
||||
" couch 128 6 0.907 0.667 0.815 0.544\n",
|
||||
" potted plant 128 14 0.739 0.786 0.823 0.48\n",
|
||||
" bed 128 3 0.985 0.333 0.83 0.441\n",
|
||||
" dining table 128 13 0.821 0.357 0.578 0.342\n",
|
||||
" toilet 128 2 1 0.988 0.995 0.846\n",
|
||||
" tv 128 2 0.57 1 0.995 0.796\n",
|
||||
" laptop 128 3 1 0 0.593 0.312\n",
|
||||
" mouse 128 2 1 0 0.089 0.0445\n",
|
||||
" remote 128 8 1 0.624 0.634 0.538\n",
|
||||
" cell phone 128 8 0.622 0.417 0.421 0.187\n",
|
||||
" microwave 128 3 0.711 1 0.995 0.766\n",
|
||||
" oven 128 5 0.329 0.4 0.43 0.282\n",
|
||||
" sink 128 6 0.437 0.333 0.338 0.265\n",
|
||||
" refrigerator 128 5 0.567 0.8 0.799 0.536\n",
|
||||
" book 128 29 0.597 0.257 0.349 0.154\n",
|
||||
" clock 128 9 0.765 0.889 0.932 0.736\n",
|
||||
" vase 128 2 0.33 1 0.995 0.895\n",
|
||||
" scissors 128 1 1 0 0.497 0.0498\n",
|
||||
" teddy bear 128 21 0.856 0.569 0.841 0.547\n",
|
||||
" toothbrush 128 5 0.8 1 0.928 0.574\n",
|
||||
"Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "15glLzbQx5u0"
|
||||
},
|
||||
"source": [
|
||||
"# 4. Visualize"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Comet Logging and Visualization 🌟 NEW\n",
|
||||
"\n",
|
||||
"[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n",
|
||||
"\n",
|
||||
"Getting started is easy:\n",
|
||||
"```shell\n",
|
||||
"pip install comet_ml # 1. install\n",
|
||||
"export COMET_API_KEY=<Your API Key> # 2. paste API key\n",
|
||||
"python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n",
|
||||
"```\n",
|
||||
"To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
|
||||
"[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
|
||||
"\n",
|
||||
"<a href=\"https://bit.ly/yolov5-readme-comet2\">\n",
|
||||
"<img alt=\"Comet Dashboard\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\" width=\"1280\"/></a>"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "nWOsI5wJR1o3"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## ClearML Logging and Automation 🌟 NEW\n",
|
||||
"\n",
|
||||
"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
|
||||
"\n",
|
||||
"- `pip install clearml`\n",
|
||||
"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
|
||||
"\n",
|
||||
"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
|
||||
"\n",
|
||||
"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
|
||||
"\n",
|
||||
"<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
|
||||
"<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Lay2WsTjNJzP"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "-WPvRbS5Swl6"
|
||||
},
|
||||
"source": [
|
||||
"## Local Logging\n",
|
||||
"\n",
|
||||
"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
|
||||
"\n",
|
||||
"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.\n",
|
||||
"\n",
|
||||
"<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Zelyeqbyt3GD"
|
||||
},
|
||||
"source": [
|
||||
"# Environments\n",
|
||||
"\n",
|
||||
"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
|
||||
"\n",
|
||||
"- **Notebooks** with free GPU: <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a> <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/models/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
|
||||
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
|
||||
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
|
||||
"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "6Qu7Iesl0p54"
|
||||
},
|
||||
"source": [
|
||||
"# Status\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "IEijrePND_2I"
|
||||
},
|
||||
"source": [
|
||||
"# Appendix\n",
|
||||
"\n",
|
||||
"Additional content below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "GMusP4OAxFu6"
|
||||
},
|
||||
"source": [
|
||||
"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
|
||||
"import torch\n",
|
||||
"\n",
|
||||
"model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n",
|
||||
"im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
|
||||
"results = model(im) # inference\n",
|
||||
"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
}
|
||||
]
|
||||
}
|
||||
96
third_party/yolov5/utils/__init__.py
vendored
Normal file
96
third_party/yolov5/utils/__init__.py
vendored
Normal file
@ -0,0 +1,96 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""utils/initialization."""
|
||||
|
||||
import contextlib
|
||||
import platform
|
||||
import threading
|
||||
|
||||
|
||||
def emojis(str=""):
|
||||
"""Returns an emoji-safe version of a string, stripped of emojis on Windows platforms."""
|
||||
return str.encode().decode("ascii", "ignore") if platform.system() == "Windows" else str
|
||||
|
||||
|
||||
class TryExcept(contextlib.ContextDecorator):
|
||||
"""A context manager and decorator for error handling that prints an optional message with emojis on exception."""
|
||||
|
||||
def __init__(self, msg=""):
|
||||
"""Initializes TryExcept with an optional message, used as a decorator or context manager for error handling."""
|
||||
self.msg = msg
|
||||
|
||||
def __enter__(self):
|
||||
"""Enter the runtime context related to this object for error handling with an optional message."""
|
||||
pass
|
||||
|
||||
def __exit__(self, exc_type, value, traceback):
|
||||
"""Context manager exit method that prints an error message with emojis if an exception occurred, always returns
|
||||
True.
|
||||
"""
|
||||
if value:
|
||||
print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
|
||||
return True
|
||||
|
||||
|
||||
def threaded(func):
|
||||
"""Decorator @threaded to run a function in a separate thread, returning the thread instance."""
|
||||
|
||||
def wrapper(*args, **kwargs):
|
||||
"""Runs the decorated function in a separate daemon thread and returns the thread instance."""
|
||||
thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
|
||||
thread.start()
|
||||
return thread
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def join_threads(verbose=False):
|
||||
"""Joins all daemon threads, optionally printing their names if verbose is True.
|
||||
|
||||
Example: atexit.register(lambda: join_threads())
|
||||
"""
|
||||
main_thread = threading.current_thread()
|
||||
for t in threading.enumerate():
|
||||
if t is not main_thread:
|
||||
if verbose:
|
||||
print(f"Joining thread {t.name}")
|
||||
t.join()
|
||||
|
||||
|
||||
def notebook_init(verbose=True):
|
||||
"""Initializes notebook environment by checking requirements, cleaning up, and displaying system info."""
|
||||
print("Checking setup...")
|
||||
|
||||
import os
|
||||
import shutil
|
||||
|
||||
from ultralytics.utils.checks import check_requirements
|
||||
|
||||
from utils.general import check_font, is_colab
|
||||
from utils.torch_utils import select_device # imports
|
||||
|
||||
check_font()
|
||||
|
||||
import psutil
|
||||
|
||||
if check_requirements("wandb", install=False):
|
||||
os.system("pip uninstall -y wandb") # eliminate unexpected account creation prompt with infinite hang
|
||||
if is_colab():
|
||||
shutil.rmtree("/content/sample_data", ignore_errors=True) # remove colab /sample_data directory
|
||||
|
||||
# System info
|
||||
display = None
|
||||
if verbose:
|
||||
gb = 1 << 30 # bytes to GiB (1024 ** 3)
|
||||
ram = psutil.virtual_memory().total
|
||||
total, _used, free = shutil.disk_usage("/")
|
||||
with contextlib.suppress(Exception): # clear display if ipython is installed
|
||||
from IPython import display
|
||||
|
||||
display.clear_output()
|
||||
s = f"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)"
|
||||
else:
|
||||
s = ""
|
||||
|
||||
select_device(newline=False)
|
||||
print(emojis(f"Setup complete ✅ {s}"))
|
||||
return display
|
||||
129
third_party/yolov5/utils/activations.py
vendored
Normal file
129
third_party/yolov5/utils/activations.py
vendored
Normal file
@ -0,0 +1,129 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""Activation functions."""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class SiLU(nn.Module):
|
||||
"""Applies the Sigmoid-weighted Linear Unit (SiLU) activation function, also known as Swish."""
|
||||
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
"""Applies the Sigmoid-weighted Linear Unit (SiLU) activation function.
|
||||
|
||||
https://arxiv.org/pdf/1606.08415.pdf.
|
||||
"""
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class Hardswish(nn.Module):
|
||||
"""Applies the Hardswish activation function, which is efficient for mobile and embedded devices."""
|
||||
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
"""Applies the Hardswish activation function, compatible with TorchScript, CoreML, and ONNX.
|
||||
|
||||
Equivalent to x * F.hardsigmoid(x)
|
||||
"""
|
||||
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX
|
||||
|
||||
|
||||
class Mish(nn.Module):
|
||||
"""Mish activation https://github.com/digantamisra98/Mish."""
|
||||
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
"""Applies the Mish activation function, a smooth alternative to ReLU."""
|
||||
return x * F.softplus(x).tanh()
|
||||
|
||||
|
||||
class MemoryEfficientMish(nn.Module):
|
||||
"""Efficiently applies the Mish activation function using custom autograd for reduced memory usage."""
|
||||
|
||||
class F(torch.autograd.Function):
|
||||
"""Implements a custom autograd function for memory-efficient Mish activation."""
|
||||
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
"""Applies the Mish activation function, a smooth ReLU alternative, to the input tensor `x`."""
|
||||
ctx.save_for_backward(x)
|
||||
return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
"""Computes the gradient of the Mish activation function with respect to input `x`."""
|
||||
x = ctx.saved_tensors[0]
|
||||
sx = torch.sigmoid(x)
|
||||
fx = F.softplus(x).tanh()
|
||||
return grad_output * (fx + x * sx * (1 - fx * fx))
|
||||
|
||||
def forward(self, x):
|
||||
"""Applies the Mish activation function to the input tensor `x`."""
|
||||
return self.F.apply(x)
|
||||
|
||||
|
||||
class FReLU(nn.Module):
|
||||
"""FReLU activation https://arxiv.org/abs/2007.11824."""
|
||||
|
||||
def __init__(self, c1, k=3): # ch_in, kernel
|
||||
"""Initializes FReLU activation with channel `c1` and kernel size `k`."""
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
|
||||
self.bn = nn.BatchNorm2d(c1)
|
||||
|
||||
def forward(self, x):
|
||||
"""Applies FReLU activation with max operation between input and BN-convolved input.
|
||||
|
||||
https://arxiv.org/abs/2007.11824
|
||||
"""
|
||||
return torch.max(x, self.bn(self.conv(x)))
|
||||
|
||||
|
||||
class AconC(nn.Module):
|
||||
"""ACON activation (activate or not) function.
|
||||
|
||||
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter See "Activate or Not: Learning
|
||||
Customized Activation" https://arxiv.org/pdf/2009.04759.pdf.
|
||||
"""
|
||||
|
||||
def __init__(self, c1):
|
||||
"""Initializes AconC with learnable parameters p1, p2, and beta for channel-wise activation control."""
|
||||
super().__init__()
|
||||
self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
|
||||
self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
|
||||
self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
|
||||
|
||||
def forward(self, x):
|
||||
"""Applies AconC activation function with learnable parameters for channel-wise control on input tensor x."""
|
||||
dpx = (self.p1 - self.p2) * x
|
||||
return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
|
||||
|
||||
|
||||
class MetaAconC(nn.Module):
|
||||
"""ACON activation (activate or not) function.
|
||||
|
||||
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter See "Activate or Not: Learning
|
||||
Customized Activation" https://arxiv.org/pdf/2009.04759.pdf.
|
||||
"""
|
||||
|
||||
def __init__(self, c1, k=1, s=1, r=16):
|
||||
"""Initializes MetaAconC with params: channel_in (c1), kernel size (k=1), stride (s=1), reduction (r=16)."""
|
||||
super().__init__()
|
||||
c2 = max(r, c1 // r)
|
||||
self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
|
||||
self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
|
||||
self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
|
||||
self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
|
||||
# self.bn1 = nn.BatchNorm2d(c2)
|
||||
# self.bn2 = nn.BatchNorm2d(c1)
|
||||
|
||||
def forward(self, x):
|
||||
"""Applies a forward pass transforming input `x` using learnable parameters and sigmoid activation."""
|
||||
y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
|
||||
# batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
|
||||
# beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable
|
||||
beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed
|
||||
dpx = (self.p1 - self.p2) * x
|
||||
return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
|
||||
429
third_party/yolov5/utils/augmentations.py
vendored
Normal file
429
third_party/yolov5/utils/augmentations.py
vendored
Normal file
@ -0,0 +1,429 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""Image augmentation functions."""
|
||||
|
||||
import math
|
||||
import random
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
|
||||
from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
|
||||
from utils.metrics import bbox_ioa
|
||||
|
||||
IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
|
||||
IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
|
||||
|
||||
|
||||
class Albumentations:
|
||||
"""Provides optional data augmentation for YOLOv5 using Albumentations library if installed."""
|
||||
|
||||
def __init__(self, size=640):
|
||||
"""Initializes Albumentations class for optional data augmentation in YOLOv5 with specified input size."""
|
||||
self.transform = None
|
||||
prefix = colorstr("albumentations: ")
|
||||
try:
|
||||
import albumentations as A
|
||||
|
||||
check_version(A.__version__, "1.0.3", hard=True) # version requirement
|
||||
|
||||
T = [
|
||||
A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
|
||||
A.Blur(p=0.01),
|
||||
A.MedianBlur(p=0.01),
|
||||
A.ToGray(p=0.01),
|
||||
A.CLAHE(p=0.01),
|
||||
A.RandomBrightnessContrast(p=0.0),
|
||||
A.RandomGamma(p=0.0),
|
||||
A.ImageCompression(quality_lower=75, p=0.0),
|
||||
] # transforms
|
||||
self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
|
||||
|
||||
LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
|
||||
except ImportError: # package not installed, skip
|
||||
pass
|
||||
except Exception as e:
|
||||
LOGGER.info(f"{prefix}{e}")
|
||||
|
||||
def __call__(self, im, labels, p=1.0):
|
||||
"""Applies transformations to an image and labels with probability `p`, returning updated image and labels."""
|
||||
if self.transform and random.random() < p:
|
||||
new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
|
||||
im, labels = new["image"], np.array([[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])])
|
||||
return im, labels
|
||||
|
||||
|
||||
def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
|
||||
"""Applies ImageNet normalization to RGB images in BCHW format, modifying them in-place if specified.
|
||||
|
||||
Example: y = (x - mean) / std
|
||||
"""
|
||||
return TF.normalize(x, mean, std, inplace=inplace)
|
||||
|
||||
|
||||
def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
|
||||
"""Reverses ImageNet normalization for BCHW format RGB images by applying `x = x * std + mean`."""
|
||||
for i in range(3):
|
||||
x[:, i] = x[:, i] * std[i] + mean[i]
|
||||
return x
|
||||
|
||||
|
||||
def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
|
||||
"""Applies HSV color-space augmentation to an image with random gains for hue, saturation, and value."""
|
||||
if hgain or sgain or vgain:
|
||||
r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
|
||||
hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
|
||||
dtype = im.dtype # uint8
|
||||
|
||||
x = np.arange(0, 256, dtype=r.dtype)
|
||||
lut_hue = ((x * r[0]) % 180).astype(dtype)
|
||||
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
|
||||
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
|
||||
|
||||
im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
|
||||
cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
|
||||
|
||||
|
||||
def hist_equalize(im, clahe=True, bgr=False):
|
||||
"""Equalizes image histogram, with optional CLAHE, for BGR or RGB image with shape (n,m,3) and range 0-255."""
|
||||
yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
|
||||
if clahe:
|
||||
c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
|
||||
yuv[:, :, 0] = c.apply(yuv[:, :, 0])
|
||||
else:
|
||||
yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
|
||||
return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
|
||||
|
||||
|
||||
def replicate(im, labels):
|
||||
"""Replicates half of the smallest object labels in an image for data augmentation.
|
||||
|
||||
Returns augmented image and labels.
|
||||
"""
|
||||
h, w = im.shape[:2]
|
||||
boxes = labels[:, 1:].astype(int)
|
||||
x1, y1, x2, y2 = boxes.T
|
||||
s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
|
||||
for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices
|
||||
x1b, y1b, x2b, y2b = boxes[i]
|
||||
bh, bw = y2b - y1b, x2b - x1b
|
||||
yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
|
||||
x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
|
||||
im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax]
|
||||
labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
|
||||
|
||||
return im, labels
|
||||
|
||||
|
||||
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
|
||||
"""Resizes and pads image to new_shape with stride-multiple constraints, returns resized image, ratio, padding."""
|
||||
shape = im.shape[:2] # current shape [height, width]
|
||||
if isinstance(new_shape, int):
|
||||
new_shape = (new_shape, new_shape)
|
||||
|
||||
# Scale ratio (new / old)
|
||||
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
|
||||
if not scaleup: # only scale down, do not scale up (for better val mAP)
|
||||
r = min(r, 1.0)
|
||||
|
||||
# Compute padding
|
||||
ratio = r, r # width, height ratios
|
||||
new_unpad = round(shape[1] * r), round(shape[0] * r)
|
||||
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
|
||||
if auto: # minimum rectangle
|
||||
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
|
||||
elif scaleFill: # stretch
|
||||
dw, dh = 0.0, 0.0
|
||||
new_unpad = (new_shape[1], new_shape[0])
|
||||
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
|
||||
|
||||
dw /= 2 # divide padding into 2 sides
|
||||
dh /= 2
|
||||
|
||||
if shape[::-1] != new_unpad: # resize
|
||||
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||||
top, bottom = round(dh - 0.1), round(dh + 0.1)
|
||||
left, right = round(dw - 0.1), round(dw + 0.1)
|
||||
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
|
||||
return im, ratio, (dw, dh)
|
||||
|
||||
|
||||
def random_perspective(
|
||||
im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0)
|
||||
):
|
||||
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
|
||||
# targets = [cls, xyxy]
|
||||
"""Applies random perspective transformation to an image, modifying the image and corresponding labels."""
|
||||
height = im.shape[0] + border[0] * 2 # shape(h,w,c)
|
||||
width = im.shape[1] + border[1] * 2
|
||||
|
||||
# Center
|
||||
C = np.eye(3)
|
||||
C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
|
||||
C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
|
||||
|
||||
# Perspective
|
||||
P = np.eye(3)
|
||||
P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
|
||||
P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
|
||||
|
||||
# Rotation and Scale
|
||||
R = np.eye(3)
|
||||
a = random.uniform(-degrees, degrees)
|
||||
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
|
||||
s = random.uniform(1 - scale, 1 + scale)
|
||||
# s = 2 ** random.uniform(-scale, scale)
|
||||
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
|
||||
|
||||
# Shear
|
||||
S = np.eye(3)
|
||||
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
|
||||
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
|
||||
|
||||
# Translation
|
||||
T = np.eye(3)
|
||||
T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
|
||||
T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
|
||||
|
||||
# Combined rotation matrix
|
||||
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
|
||||
if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
|
||||
if perspective:
|
||||
im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
|
||||
else: # affine
|
||||
im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
|
||||
|
||||
if n := len(targets):
|
||||
use_segments = any(x.any() for x in segments) and len(segments) == n
|
||||
new = np.zeros((n, 4))
|
||||
if use_segments: # warp segments
|
||||
segments = resample_segments(segments) # upsample
|
||||
for i, segment in enumerate(segments):
|
||||
xy = np.ones((len(segment), 3))
|
||||
xy[:, :2] = segment
|
||||
xy = xy @ M.T # transform
|
||||
xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
|
||||
|
||||
# clip
|
||||
new[i] = segment2box(xy, width, height)
|
||||
|
||||
else: # warp boxes
|
||||
xy = np.ones((n * 4, 3))
|
||||
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
|
||||
xy = xy @ M.T # transform
|
||||
xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
|
||||
|
||||
# create new boxes
|
||||
x = xy[:, [0, 2, 4, 6]]
|
||||
y = xy[:, [1, 3, 5, 7]]
|
||||
new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
||||
|
||||
# clip
|
||||
new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
|
||||
new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
|
||||
|
||||
# filter candidates
|
||||
i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
|
||||
targets = targets[i]
|
||||
targets[:, 1:5] = new[i]
|
||||
|
||||
return im, targets
|
||||
|
||||
|
||||
def copy_paste(im, labels, segments, p=0.5):
|
||||
"""Applies Copy-Paste augmentation by flipping and merging segments and labels on an image.
|
||||
|
||||
Details at https://arxiv.org/abs/2012.07177.
|
||||
"""
|
||||
n = len(segments)
|
||||
if p and n:
|
||||
_h, w, _c = im.shape # height, width, channels
|
||||
im_new = np.zeros(im.shape, np.uint8)
|
||||
for j in random.sample(range(n), k=round(p * n)):
|
||||
l, s = labels[j], segments[j]
|
||||
box = w - l[3], l[2], w - l[1], l[4]
|
||||
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
|
||||
if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
|
||||
labels = np.concatenate((labels, [[l[0], *box]]), 0)
|
||||
segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
|
||||
cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)
|
||||
|
||||
result = cv2.flip(im, 1) # augment segments (flip left-right)
|
||||
i = cv2.flip(im_new, 1).astype(bool)
|
||||
im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
|
||||
|
||||
return im, labels, segments
|
||||
|
||||
|
||||
def cutout(im, labels, p=0.5):
|
||||
"""Applies cutout augmentation to an image with optional label adjustment, using random masks of varying sizes.
|
||||
|
||||
Details at https://arxiv.org/abs/1708.04552.
|
||||
"""
|
||||
if random.random() < p:
|
||||
h, w = im.shape[:2]
|
||||
scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
|
||||
for s in scales:
|
||||
mask_h = random.randint(1, int(h * s)) # create random masks
|
||||
mask_w = random.randint(1, int(w * s))
|
||||
|
||||
# box
|
||||
xmin = max(0, random.randint(0, w) - mask_w // 2)
|
||||
ymin = max(0, random.randint(0, h) - mask_h // 2)
|
||||
xmax = min(w, xmin + mask_w)
|
||||
ymax = min(h, ymin + mask_h)
|
||||
|
||||
# apply random color mask
|
||||
im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
|
||||
|
||||
# return unobscured labels
|
||||
if len(labels) and s > 0.03:
|
||||
box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
|
||||
ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area
|
||||
labels = labels[ioa < 0.60] # remove >60% obscured labels
|
||||
|
||||
return labels
|
||||
|
||||
|
||||
def mixup(im, labels, im2, labels2):
|
||||
"""Applies MixUp augmentation by blending images and labels.
|
||||
|
||||
See https://arxiv.org/pdf/1710.09412.pdf for details.
|
||||
"""
|
||||
r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
|
||||
im = (im * r + im2 * (1 - r)).astype(np.uint8)
|
||||
labels = np.concatenate((labels, labels2), 0)
|
||||
return im, labels
|
||||
|
||||
|
||||
def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
|
||||
"""Filters bounding box candidates by minimum width-height threshold `wh_thr` (pixels), aspect ratio threshold
|
||||
`ar_thr`, and area ratio threshold `area_thr`.
|
||||
|
||||
box1(4,n) is before augmentation, box2(4,n) is after augmentation.
|
||||
"""
|
||||
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
|
||||
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
|
||||
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
|
||||
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
|
||||
|
||||
|
||||
def classify_albumentations(
|
||||
augment=True,
|
||||
size=224,
|
||||
scale=(0.08, 1.0),
|
||||
ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33
|
||||
hflip=0.5,
|
||||
vflip=0.0,
|
||||
jitter=0.4,
|
||||
mean=IMAGENET_MEAN,
|
||||
std=IMAGENET_STD,
|
||||
auto_aug=False,
|
||||
):
|
||||
# YOLOv5 classification Albumentations (optional, only used if package is installed)
|
||||
"""Sets up Albumentations transforms for YOLOv5 classification tasks depending on augmentation settings."""
|
||||
prefix = colorstr("albumentations: ")
|
||||
try:
|
||||
import albumentations as A
|
||||
from albumentations.pytorch import ToTensorV2
|
||||
|
||||
check_version(A.__version__, "1.0.3", hard=True) # version requirement
|
||||
if augment: # Resize and crop
|
||||
T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
|
||||
if auto_aug:
|
||||
# TODO: implement AugMix, AutoAug & RandAug in albumentation
|
||||
LOGGER.info(f"{prefix}auto augmentations are currently not supported")
|
||||
else:
|
||||
if hflip > 0:
|
||||
T += [A.HorizontalFlip(p=hflip)]
|
||||
if vflip > 0:
|
||||
T += [A.VerticalFlip(p=vflip)]
|
||||
if jitter > 0:
|
||||
color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, saturation, 0 hue
|
||||
T += [A.ColorJitter(*color_jitter, 0)]
|
||||
else: # Use fixed crop for eval set (reproducibility)
|
||||
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
|
||||
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
|
||||
LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
|
||||
return A.Compose(T)
|
||||
|
||||
except ImportError: # package not installed, skip
|
||||
LOGGER.warning(f"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)")
|
||||
except Exception as e:
|
||||
LOGGER.info(f"{prefix}{e}")
|
||||
|
||||
|
||||
def classify_transforms(size=224):
|
||||
"""Applies a series of transformations including center crop, ToTensor, and normalization for classification."""
|
||||
assert isinstance(size, int), f"ERROR: classify_transforms size {size} must be integer, not (list, tuple)"
|
||||
# T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
|
||||
|
||||
class LetterBox:
|
||||
"""Resizes and pads images to specified dimensions while maintaining aspect ratio for YOLOv5 preprocessing."""
|
||||
|
||||
def __init__(self, size=(640, 640), auto=False, stride=32):
|
||||
"""Initializes a LetterBox object for YOLOv5 image preprocessing with optional auto sizing and stride
|
||||
adjustment.
|
||||
"""
|
||||
super().__init__()
|
||||
self.h, self.w = (size, size) if isinstance(size, int) else size
|
||||
self.auto = auto # pass max size integer, automatically solve for short side using stride
|
||||
self.stride = stride # used with auto
|
||||
|
||||
def __call__(self, im):
|
||||
"""Resizes and pads input image `im` (HWC format) to specified dimensions, maintaining aspect ratio.
|
||||
|
||||
im = np.array HWC
|
||||
"""
|
||||
imh, imw = im.shape[:2]
|
||||
r = min(self.h / imh, self.w / imw) # ratio of new/old
|
||||
h, w = round(imh * r), round(imw * r) # resized image
|
||||
hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
|
||||
top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
|
||||
im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
|
||||
im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
|
||||
return im_out
|
||||
|
||||
|
||||
class CenterCrop:
|
||||
"""Applies center crop to an image, resizing it to the specified size while maintaining aspect ratio."""
|
||||
|
||||
def __init__(self, size=640):
|
||||
"""Initializes CenterCrop for image preprocessing, accepting single int or tuple for size, defaults to 640."""
|
||||
super().__init__()
|
||||
self.h, self.w = (size, size) if isinstance(size, int) else size
|
||||
|
||||
def __call__(self, im):
|
||||
"""Applies center crop to the input image and resizes it to a specified size, maintaining aspect ratio.
|
||||
|
||||
im = np.array HWC
|
||||
"""
|
||||
imh, imw = im.shape[:2]
|
||||
m = min(imh, imw) # min dimension
|
||||
top, left = (imh - m) // 2, (imw - m) // 2
|
||||
return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
|
||||
class ToTensor:
|
||||
"""Converts BGR np.array image from HWC to RGB CHW format, normalizes to [0, 1], and supports FP16 if half=True."""
|
||||
|
||||
def __init__(self, half=False):
|
||||
"""Initializes ToTensor for YOLOv5 image preprocessing, with optional half precision (half=True for FP16)."""
|
||||
super().__init__()
|
||||
self.half = half
|
||||
|
||||
def __call__(self, im):
|
||||
"""Converts BGR np.array image from HWC to RGB CHW format, and normalizes to [0, 1], with support for FP16 if
|
||||
`half=True`.
|
||||
|
||||
im = np.array HWC in BGR order
|
||||
"""
|
||||
im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
|
||||
im = torch.from_numpy(im) # to torch
|
||||
im = im.half() if self.half else im.float() # uint8 to fp16/32
|
||||
im /= 255.0 # 0-255 to 0.0-1.0
|
||||
return im
|
||||
174
third_party/yolov5/utils/autoanchor.py
vendored
Normal file
174
third_party/yolov5/utils/autoanchor.py
vendored
Normal file
@ -0,0 +1,174 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""AutoAnchor utils."""
|
||||
|
||||
import random
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils import TryExcept
|
||||
from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
|
||||
|
||||
PREFIX = colorstr("AutoAnchor: ")
|
||||
|
||||
|
||||
def check_anchor_order(m):
|
||||
"""Checks and corrects anchor order against stride in YOLOv5 Detect() module if necessary."""
|
||||
a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer
|
||||
da = a[-1] - a[0] # delta a
|
||||
ds = m.stride[-1] - m.stride[0] # delta s
|
||||
if da and (da.sign() != ds.sign()): # same order
|
||||
LOGGER.info(f"{PREFIX}Reversing anchor order")
|
||||
m.anchors[:] = m.anchors.flip(0)
|
||||
|
||||
|
||||
@TryExcept(f"{PREFIX}ERROR")
|
||||
def check_anchors(dataset, model, thr=4.0, imgsz=640):
|
||||
"""Evaluates anchor fit to dataset and adjusts if necessary, supporting customizable threshold and image size."""
|
||||
m = model.module.model[-1] if hasattr(model, "module") else model.model[-1] # Detect()
|
||||
shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
|
||||
scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale
|
||||
wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh
|
||||
|
||||
def metric(k): # compute metric
|
||||
"""Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation."""
|
||||
r = wh[:, None] / k[None]
|
||||
x = torch.min(r, 1 / r).min(2)[0] # ratio metric
|
||||
best = x.max(1)[0] # best_x
|
||||
aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold
|
||||
bpr = (best > 1 / thr).float().mean() # best possible recall
|
||||
return bpr, aat
|
||||
|
||||
stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides
|
||||
anchors = m.anchors.clone() * stride # current anchors
|
||||
bpr, aat = metric(anchors.cpu().view(-1, 2))
|
||||
s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). "
|
||||
if bpr > 0.98: # threshold to recompute
|
||||
LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅")
|
||||
else:
|
||||
LOGGER.info(f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...")
|
||||
na = m.anchors.numel() // 2 # number of anchors
|
||||
anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
|
||||
new_bpr = metric(anchors)[0]
|
||||
if new_bpr > bpr: # replace anchors
|
||||
anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
|
||||
m.anchors[:] = anchors.clone().view_as(m.anchors)
|
||||
check_anchor_order(m) # must be in pixel-space (not grid-space)
|
||||
m.anchors /= stride
|
||||
s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)"
|
||||
else:
|
||||
s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)"
|
||||
LOGGER.info(s)
|
||||
|
||||
|
||||
def kmean_anchors(dataset="./data/coco128.yaml", n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
|
||||
"""Creates kmeans-evolved anchors from training dataset.
|
||||
|
||||
Args:
|
||||
dataset: path to data.yaml, or a loaded dataset
|
||||
n: number of anchors
|
||||
img_size: image size used for training
|
||||
thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
|
||||
gen: generations to evolve anchors using genetic algorithm
|
||||
verbose: print all results
|
||||
|
||||
Returns:
|
||||
k: kmeans evolved anchors
|
||||
|
||||
Examples:
|
||||
from utils.autoanchor import *; _ = kmean_anchors()
|
||||
"""
|
||||
from scipy.cluster.vq import kmeans
|
||||
|
||||
npr = np.random
|
||||
thr = 1 / thr
|
||||
|
||||
def metric(k, wh): # compute metrics
|
||||
"""Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation."""
|
||||
r = wh[:, None] / k[None]
|
||||
x = torch.min(r, 1 / r).min(2)[0] # ratio metric
|
||||
# x = wh_iou(wh, torch.tensor(k)) # iou metric
|
||||
return x, x.max(1)[0] # x, best_x
|
||||
|
||||
def anchor_fitness(k): # mutation fitness
|
||||
"""Evaluates fitness of YOLOv5 anchors by computing recall and ratio metrics for an anchor evolution process."""
|
||||
_, best = metric(torch.tensor(k, dtype=torch.float32), wh)
|
||||
return (best * (best > thr).float()).mean() # fitness
|
||||
|
||||
def print_results(k, verbose=True):
|
||||
"""Sorts and logs kmeans-evolved anchor metrics and best possible recall values for YOLOv5 anchor evaluation."""
|
||||
k = k[np.argsort(k.prod(1))] # sort small to large
|
||||
x, best = metric(k, wh0)
|
||||
bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
|
||||
s = (
|
||||
f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n"
|
||||
f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, "
|
||||
f"past_thr={x[x > thr].mean():.3f}-mean: "
|
||||
)
|
||||
for x in k:
|
||||
s += "%i,%i, " % (round(x[0]), round(x[1]))
|
||||
if verbose:
|
||||
LOGGER.info(s[:-2])
|
||||
return k
|
||||
|
||||
if isinstance(dataset, str): # *.yaml file
|
||||
with open(dataset, errors="ignore") as f:
|
||||
data_dict = yaml.safe_load(f) # model dict
|
||||
from utils.dataloaders import LoadImagesAndLabels
|
||||
|
||||
dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True)
|
||||
|
||||
# Get label wh
|
||||
shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
|
||||
wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh
|
||||
|
||||
# Filter
|
||||
i = (wh0 < 3.0).any(1).sum()
|
||||
if i:
|
||||
LOGGER.info(f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size")
|
||||
wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels
|
||||
# wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1
|
||||
|
||||
# Kmeans init
|
||||
try:
|
||||
LOGGER.info(f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...")
|
||||
assert n <= len(wh) # apply overdetermined constraint
|
||||
s = wh.std(0) # sigmas for whitening
|
||||
k = kmeans(wh / s, n, iter=30)[0] * s # points
|
||||
assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar
|
||||
except Exception:
|
||||
LOGGER.warning(f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init")
|
||||
k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init
|
||||
wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
|
||||
k = print_results(k, verbose=False)
|
||||
|
||||
# Plot
|
||||
# k, d = [None] * 20, [None] * 20
|
||||
# for i in tqdm(range(1, 21)):
|
||||
# k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance
|
||||
# fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
|
||||
# ax = ax.ravel()
|
||||
# ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
|
||||
# fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh
|
||||
# ax[0].hist(wh[wh[:, 0]<100, 0],400)
|
||||
# ax[1].hist(wh[wh[:, 1]<100, 1],400)
|
||||
# fig.savefig('wh.png', dpi=200)
|
||||
|
||||
# Evolve
|
||||
f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
|
||||
pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
for _ in pbar:
|
||||
v = np.ones(sh)
|
||||
while (v == 1).all(): # mutate until a change occurs (prevent duplicates)
|
||||
v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
|
||||
kg = (k.copy() * v).clip(min=2.0)
|
||||
fg = anchor_fitness(kg)
|
||||
if fg > f:
|
||||
f, k = fg, kg.copy()
|
||||
pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}"
|
||||
if verbose:
|
||||
print_results(k, verbose)
|
||||
|
||||
return print_results(k).astype(np.float32)
|
||||
70
third_party/yolov5/utils/autobatch.py
vendored
Normal file
70
third_party/yolov5/utils/autobatch.py
vendored
Normal file
@ -0,0 +1,70 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""Auto-batch utils."""
|
||||
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from utils.general import LOGGER, colorstr
|
||||
from utils.torch_utils import profile
|
||||
|
||||
|
||||
def check_train_batch_size(model, imgsz=640, amp=True):
|
||||
"""Checks and computes optimal training batch size for YOLOv5 model, given image size and AMP setting."""
|
||||
with torch.cuda.amp.autocast(amp):
|
||||
return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
|
||||
|
||||
|
||||
def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
|
||||
"""Estimates optimal YOLOv5 batch size using `fraction` of CUDA memory."""
|
||||
# Usage:
|
||||
# import torch
|
||||
# from utils.autobatch import autobatch
|
||||
# model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
|
||||
# print(autobatch(model))
|
||||
|
||||
# Check device
|
||||
prefix = colorstr("AutoBatch: ")
|
||||
LOGGER.info(f"{prefix}Computing optimal batch size for --imgsz {imgsz}")
|
||||
device = next(model.parameters()).device # get model device
|
||||
if device.type == "cpu":
|
||||
LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}")
|
||||
return batch_size
|
||||
if torch.backends.cudnn.benchmark:
|
||||
LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}")
|
||||
return batch_size
|
||||
|
||||
# Inspect CUDA memory
|
||||
gb = 1 << 30 # bytes to GiB (1024 ** 3)
|
||||
d = str(device).upper() # 'CUDA:0'
|
||||
properties = torch.cuda.get_device_properties(device) # device properties
|
||||
t = properties.total_memory / gb # GiB total
|
||||
r = torch.cuda.memory_reserved(device) / gb # GiB reserved
|
||||
a = torch.cuda.memory_allocated(device) / gb # GiB allocated
|
||||
f = t - (r + a) # GiB free
|
||||
LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free")
|
||||
|
||||
# Profile batch sizes
|
||||
batch_sizes = [1, 2, 4, 8, 16]
|
||||
try:
|
||||
img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
|
||||
results = profile(img, model, n=3, device=device)
|
||||
except Exception as e:
|
||||
LOGGER.warning(f"{prefix}{e}")
|
||||
|
||||
# Fit a solution
|
||||
y = [x[2] for x in results if x] # memory [2]
|
||||
p = np.polyfit(batch_sizes[: len(y)], y, deg=1) # first degree polynomial fit
|
||||
b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
|
||||
if None in results: # some sizes failed
|
||||
i = results.index(None) # first fail index
|
||||
if b >= batch_sizes[i]: # y intercept above failure point
|
||||
b = batch_sizes[max(i - 1, 0)] # select prior safe point
|
||||
if b < 1 or b > 1024: # b outside of safe range
|
||||
b = batch_size
|
||||
LOGGER.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.")
|
||||
|
||||
fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
|
||||
LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅")
|
||||
return b
|
||||
1
third_party/yolov5/utils/aws/__init__.py
vendored
Normal file
1
third_party/yolov5/utils/aws/__init__.py
vendored
Normal file
@ -0,0 +1 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
33
third_party/yolov5/utils/aws/mime.sh
vendored
Normal file
33
third_party/yolov5/utils/aws/mime.sh
vendored
Normal file
@ -0,0 +1,33 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/
|
||||
# This script will run on every instance restart, not only on first start
|
||||
# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---
|
||||
|
||||
Content-Type: multipart/mixed
|
||||
boundary="//"
|
||||
MIME-Version: 1.0
|
||||
|
||||
--//
|
||||
Content-Type: text/cloud-config
|
||||
charset="us-ascii"
|
||||
MIME-Version: 1.0
|
||||
Content-Transfer-Encoding: 7bit
|
||||
Content-Disposition: attachment
|
||||
filename="cloud-config.txt"
|
||||
|
||||
#cloud-config
|
||||
cloud_final_modules:
|
||||
- [scripts-user, always]
|
||||
|
||||
--//
|
||||
Content-Type: text/x-shellscript
|
||||
charset="us-ascii"
|
||||
MIME-Version: 1.0
|
||||
Content-Transfer-Encoding: 7bit
|
||||
Content-Disposition: attachment
|
||||
filename="userdata.txt"
|
||||
|
||||
#!/bin/bash
|
||||
# --- paste contents of userdata.sh here ---
|
||||
--//
|
||||
43
third_party/yolov5/utils/aws/resume.py
vendored
Normal file
43
third_party/yolov5/utils/aws/resume.py
vendored
Normal file
@ -0,0 +1,43 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Resume all interrupted trainings in yolov5/ dir including DDP trainings
|
||||
# Usage: $ python utils/aws/resume.py
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import yaml
|
||||
from ultralytics.utils.patches import torch_load
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[2] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
|
||||
port = 0 # --master_port
|
||||
path = Path("").resolve()
|
||||
for last in path.rglob("*/**/last.pt"):
|
||||
ckpt = torch_load(last)
|
||||
if ckpt["optimizer"] is None:
|
||||
continue
|
||||
|
||||
# Load opt.yaml
|
||||
with open(last.parent.parent / "opt.yaml", errors="ignore") as f:
|
||||
opt = yaml.safe_load(f)
|
||||
|
||||
# Get device count
|
||||
d = opt["device"].split(",") # devices
|
||||
nd = len(d) # number of devices
|
||||
ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel
|
||||
|
||||
if ddp: # multi-GPU
|
||||
port += 1
|
||||
cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}"
|
||||
else: # single-GPU
|
||||
cmd = f"python train.py --resume {last}"
|
||||
|
||||
cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread
|
||||
print(cmd)
|
||||
os.system(cmd)
|
||||
29
third_party/yolov5/utils/aws/userdata.sh
vendored
Normal file
29
third_party/yolov5/utils/aws/userdata.sh
vendored
Normal file
@ -0,0 +1,29 @@
|
||||
#!/bin/bash
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
|
||||
# This script will run only once on first instance start (for a re-start script see mime.sh)
|
||||
# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
|
||||
# Use >300 GB SSD
|
||||
|
||||
cd home/ubuntu
|
||||
if [ ! -d yolov5 ]; then
|
||||
echo "Running first-time script." # install dependencies, download COCO, pull Docker
|
||||
git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5
|
||||
cd yolov5
|
||||
bash data/scripts/get_coco.sh && echo "COCO done." &
|
||||
sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
|
||||
python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
|
||||
wait && echo "All tasks done." # finish background tasks
|
||||
else
|
||||
echo "Running re-start script." # resume interrupted runs
|
||||
i=0
|
||||
list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
|
||||
while IFS= read -r id; do
|
||||
((i++))
|
||||
echo "restarting container $i: $id"
|
||||
sudo docker start $id
|
||||
# sudo docker exec -it $id python train.py --resume # single-GPU
|
||||
sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
|
||||
done <<< "$list"
|
||||
fi
|
||||
69
third_party/yolov5/utils/callbacks.py
vendored
Normal file
69
third_party/yolov5/utils/callbacks.py
vendored
Normal file
@ -0,0 +1,69 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
"""Callback utils."""
|
||||
|
||||
import threading
|
||||
|
||||
|
||||
class Callbacks:
|
||||
"""Handles all registered callbacks for YOLOv5 Hooks."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initializes a Callbacks object to manage registered YOLOv5 training event hooks."""
|
||||
self._callbacks = {
|
||||
"on_pretrain_routine_start": [],
|
||||
"on_pretrain_routine_end": [],
|
||||
"on_train_start": [],
|
||||
"on_train_epoch_start": [],
|
||||
"on_train_batch_start": [],
|
||||
"optimizer_step": [],
|
||||
"on_before_zero_grad": [],
|
||||
"on_train_batch_end": [],
|
||||
"on_train_epoch_end": [],
|
||||
"on_val_start": [],
|
||||
"on_val_batch_start": [],
|
||||
"on_val_image_end": [],
|
||||
"on_val_batch_end": [],
|
||||
"on_val_end": [],
|
||||
"on_fit_epoch_end": [], # fit = train + val
|
||||
"on_model_save": [],
|
||||
"on_train_end": [],
|
||||
"on_params_update": [],
|
||||
"teardown": [],
|
||||
}
|
||||
self.stop_training = False # set True to interrupt training
|
||||
|
||||
def register_action(self, hook, name="", callback=None):
|
||||
"""Register a new action to a callback hook.
|
||||
|
||||
Args:
|
||||
hook: The callback hook name to register the action to
|
||||
name: The name of the action for later reference
|
||||
callback: The callback to fire
|
||||
"""
|
||||
assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
|
||||
assert callable(callback), f"callback '{callback}' is not callable"
|
||||
self._callbacks[hook].append({"name": name, "callback": callback})
|
||||
|
||||
def get_registered_actions(self, hook=None):
|
||||
"""Returns all the registered actions by callback hook.
|
||||
|
||||
Args:
|
||||
hook: The name of the hook to check, defaults to all
|
||||
"""
|
||||
return self._callbacks[hook] if hook else self._callbacks
|
||||
|
||||
def run(self, hook, *args, thread=False, **kwargs):
|
||||
"""Loop through the registered actions and fire all callbacks on main thread.
|
||||
|
||||
Args:
|
||||
hook: The name of the hook to check, defaults to all
|
||||
args: Arguments to receive from YOLOv5
|
||||
thread: (boolean) Run callbacks in daemon thread
|
||||
kwargs: Keyword Arguments to receive from YOLOv5
|
||||
"""
|
||||
assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
|
||||
for logger in self._callbacks[hook]:
|
||||
if thread:
|
||||
threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start()
|
||||
else:
|
||||
logger["callback"](*args, **kwargs)
|
||||
1365
third_party/yolov5/utils/dataloaders.py
vendored
Normal file
1365
third_party/yolov5/utils/dataloaders.py
vendored
Normal file
File diff suppressed because it is too large
Load Diff
74
third_party/yolov5/utils/docker/Dockerfile
vendored
Normal file
74
third_party/yolov5/utils/docker/Dockerfile
vendored
Normal file
@ -0,0 +1,74 @@
|
||||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
|
||||
# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
|
||||
|
||||
# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
|
||||
FROM pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime
|
||||
|
||||
# Downloads to user config dir
|
||||
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
|
||||
|
||||
# Install linux packages
|
||||
ENV DEBIAN_FRONTEND noninteractive
|
||||
RUN apt update
|
||||
RUN TZ=Etc/UTC apt install -y tzdata
|
||||
RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg
|
||||
# RUN alias python=python3
|
||||
|
||||
# Security updates
|
||||
# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
|
||||
RUN apt upgrade --no-install-recommends -y openssl
|
||||
|
||||
# Create working directory
|
||||
RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
|
||||
WORKDIR /usr/src/app
|
||||
|
||||
# Copy contents
|
||||
COPY . /usr/src/app
|
||||
|
||||
# Install pip packages
|
||||
COPY requirements.txt .
|
||||
RUN python3 -m pip install --upgrade pip wheel
|
||||
RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
|
||||
coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'
|
||||
# tensorflow tensorflowjs \
|
||||
|
||||
# Set environment variables
|
||||
ENV OMP_NUM_THREADS=1
|
||||
|
||||
# Cleanup
|
||||
ENV DEBIAN_FRONTEND teletype
|
||||
|
||||
|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
|
||||
|
||||
# Pull and Run
|
||||
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
|
||||
|
||||
# Pull and Run with local directory access
|
||||
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
|
||||
|
||||
# Kill all
|
||||
# sudo docker kill $(sudo docker ps -q)
|
||||
|
||||
# Kill all image-based
|
||||
# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
|
||||
|
||||
# DockerHub tag update
|
||||
# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
|
||||
|
||||
# Clean up
|
||||
# sudo docker system prune -a --volumes
|
||||
|
||||
# Update Ubuntu drivers
|
||||
# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
|
||||
|
||||
# DDP test
|
||||
# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
|
||||
|
||||
# GCP VM from Image
|
||||
# docker.io/ultralytics/yolov5:latest
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user