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# 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
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*.ipynb linguist-vendored
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# YOLOv5 🚀 Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
- Demonstrating empathy and kindness toward other people
- Being respectful of differing opinions, viewpoints, and experiences
- Giving and gracefully accepting constructive feedback
- Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
- Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
- The use of sexualized language or imagery, and sexual attention or
advances of any kind
- Trolling, insulting or derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or email
address, without their explicit permission
- Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
hello@ultralytics.com.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
[homepage]: https://www.contributor-covenant.org
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name: 🐛 Bug Report
# title: " "
description: Problems with YOLOv5
labels: [bug, triage]
body:
- type: markdown
attributes:
value: |
Thank you for submitting a YOLOv5 🐛 Bug Report!
- type: checkboxes
attributes:
label: Search before asking
description: >
Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar bug report already exists.
options:
- label: >
I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report.
required: true
- type: dropdown
attributes:
label: YOLOv5 Component
description: |
Please select the part of YOLOv5 where you found the bug.
multiple: true
options:
- "Training"
- "Validation"
- "Detection"
- "Export"
- "PyTorch Hub"
- "Multi-GPU"
- "Evolution"
- "Integrations"
- "Other"
validations:
required: false
- type: textarea
attributes:
label: Bug
description: Provide console output with error messages and/or screenshots of the bug.
placeholder: |
💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
validations:
required: true
- type: textarea
attributes:
label: Environment
description: Please specify the software and hardware you used to produce the bug.
placeholder: |
- YOLO: YOLOv5 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)
- OS: Ubuntu 20.04
- Python: 3.9.0
validations:
required: false
- type: textarea
attributes:
label: Minimal Reproducible Example
description: >
When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem.
This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example).
placeholder: |
```
# Code to reproduce your issue here
```
validations:
required: false
- type: textarea
attributes:
label: Additional
description: Anything else you would like to share?
- type: checkboxes
attributes:
label: Are you willing to submit a PR?
description: >
(Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature.
See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started.
options:
- label: Yes I'd like to help by submitting a PR!
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blank_issues_enabled: true
contact_links:
- name: 💬 Forum
url: https://community.ultralytics.com/
about: Ask on Ultralytics Community Forum
- name: Stack Overflow
url: https://stackoverflow.com/search?q=YOLOv5
about: Ask on Stack Overflow with 'YOLOv5' tag
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name: 🚀 Feature Request
description: Suggest a YOLOv5 idea
# title: " "
labels: [enhancement]
body:
- type: markdown
attributes:
value: |
Thank you for submitting a YOLOv5 🚀 Feature Request!
- type: checkboxes
attributes:
label: Search before asking
description: >
Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar feature request already exists.
options:
- label: >
I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar feature requests.
required: true
- type: textarea
attributes:
label: Description
description: A short description of your feature.
placeholder: |
What new feature would you like to see in YOLOv5?
validations:
required: true
- type: textarea
attributes:
label: Use case
description: |
Describe the use case of your feature request. It will help us understand and prioritize the feature request.
placeholder: |
How would this feature be used, and who would use it?
- type: textarea
attributes:
label: Additional
description: Anything else you would like to share?
- type: checkboxes
attributes:
label: Are you willing to submit a PR?
description: >
(Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature.
See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started.
options:
- label: Yes I'd like to help by submitting a PR!
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name: ❓ Question
description: Ask a YOLOv5 question
# title: " "
labels: [question]
body:
- type: markdown
attributes:
value: |
Thank you for asking a YOLOv5 ❓ Question!
- type: checkboxes
attributes:
label: Search before asking
description: >
Please search the [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) to see if a similar question already exists.
options:
- label: >
I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
required: true
- type: textarea
attributes:
label: Question
description: What is your question?
placeholder: |
💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
validations:
required: true
- type: textarea
attributes:
label: Additional
description: Anything else you would like to share?
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<!--
Thank you for submitting a YOLOv5 🚀 Pull Request! We want to make contributing to YOLOv5 as easy and transparent as possible. A few tips to get you started:
- Search existing YOLOv5 [PRs](https://github.com/ultralytics/yolov5/pull) to see if a similar PR already exists.
- Link this PR to a YOLOv5 [issue](https://github.com/ultralytics/yolov5/issues) to help us understand what bug fix or feature is being implemented.
- Provide before and after profiling/inference/training results to help us quantify the improvement your PR provides (if applicable).
Please see our ✅ [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) for more details.
-->
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<div align="center">
<p>
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png"></a>
</p>
[English](../README.md) | 简体中文
<br>
<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"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></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>
<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/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
<br>
<p>
YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了<a href="https://ultralytics.com">Ultralytics</a>对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。
</p>
<div align="center">
<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a>
</div>
</div>
## <div align="center">文件</div>
请参阅[YOLOv5 Docs](https://docs.ultralytics.com)，了解有关训练、测试和部署的完整文件。
## <div align="center">快速开始案例</div>
<details open>
<summary>安装</summary>
在[**Python>=3.7.0**](https://www.python.org/) 的环境中克隆版本仓并安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)，包括[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)。
```bash
git clone https://github.com/ultralytics/yolov5 # 克隆
cd yolov5
pip install -r requirements.txt # 安装
```
</details>
<details open>
<summary>推理</summary>
YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从最新YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases)下载。
```python
import torch
# 模型
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom
# 图像
img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
# 推理
results = model(img)
# 结果
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
</details>
<details>
<summary>用 detect.py 进行推理</summary>
`detect.py` 在各种数据源上运行推理, 其会从最新的 YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中自动下载 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 并将检测结果保存到 `runs/detect` 目录。
```bash
python detect.py --source 0 # 网络摄像头
img.jpg # 图像
vid.mp4 # 视频
path/ # 文件夹
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP 流
```
</details>
<details>
<summary>训练</summary>
以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 V100-16GB。
```bash
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
</details>
<details open>
<summary>教程</summary>
- [训练自定义数据集](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 推荐
- [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
推荐
- [多GPU训练](https://github.com/ultralytics/yolov5/issues/475)
- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 新
- [TFLite, ONNX, CoreML, TensorRT 输出](https://github.com/ultralytics/yolov5/issues/251) 🚀
- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303)
- [模型集成](https://github.com/ultralytics/yolov5/issues/318)
- [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304)
- [超参数进化](https://github.com/ultralytics/yolov5/issues/607)
- [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314)
- [架构概要](https://github.com/ultralytics/yolov5/issues/6998) 🌟 新
- [使用Weights & Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289)
- [Roboflow：数据集，标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975) 🌟 新
- [使用ClearML 记录实验](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 新
- [Deci 平台](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 新
</details>
## <div align="center">Integrations</div>
<br>
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/master/im/integrations-loop.png"></a>
<br>
<br>
<div align="center">
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
<a href="https://cutt.ly/yolov5-readme-clearml">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
<a href="https://bit.ly/yolov5-readme-comet">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-comet.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
<a href="https://bit.ly/yolov5-deci-platform">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
</div>
|Roboflow|ClearML ⭐ NEW|Comet ⭐ NEW|Deci ⭐ NEW|
|:-:|:-:|:-:|:-:|
|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|
## <div align="center">Ultralytics HUB</div>
[Ultralytics HUB](https://bit.ly/ultralytics_hub) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv5 🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now!
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/master/im/ultralytics-hub.png"></a>
## <div align="center">为什么选择 YOLOv5</div>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
<details>
<summary>YOLOv5-P5 640 图像 (点击扩展)</summary>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
</details>
<details>
<summary>图片注释 (点击扩展)</summary>
- **COCO AP val** 表示 mAP@0.5:0.95 在5000张图像的[COCO val2017](http://cocodataset.org)数据集上，在256到1536的不同推理大小上测量的指标。
- **GPU Speed** 衡量的是在 [COCO val2017](http://cocodataset.org) 数据集上使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例在批量大小为32时每张图像的平均推理时间。
- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。
- 复现 mAP 方法: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
</details>
### 预训练检查点
| 模型 | 规模<br><sup>(像素) | mAP<sup>验证<br>0.5:0.95 | mAP<sup>验证<br>0.5 | 速度<br><sup>CPU b1<br>(ms) | 速度<br><sup>V100 b1<br>(ms) | 速度<br><sup>V100 b32<br>(ms) | 参数<br><sup>(M) | 浮点运算<br><sup>@640 (B) |
|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| | | | | | | | | |
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt)<br>+ [TTA][TTA] | 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>表格注释 (点击扩展)</summary>
- 所有检查点都以默认设置训练到300个时期. Nano和Small模型用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, 其他模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
- **mAP<sup>val</sup>** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。
<br>复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
- 使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) 实例对COCO val图像的平均速度。不包括NMS时间（~1 ms/img)
<br>复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1`
- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强.
<br>复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
</details>
## <div align="center">分类 ⭐ 新</div>
YOLOv5发布的[v6.2版本](https://github.com/ultralytics/yolov5/releases) 支持训练，验证，预测和输出分类模型！这使得训练分类器模型非常简单。点击下面开始尝试！
<details>
<summary>分类检查点 (点击展开)</summary>
<br>
我们在ImageNet上使用了4xA100的实例训练YOLOv5-cls分类模型90个epochs，并以相同的默认设置同时训练了ResNet和EfficientNet模型来进行比较。我们将所有的模型导出到ONNX FP32进行CPU速度测试，又导出到TensorRT FP16进行GPU速度测试。最后，为了方便重现，我们在[Google Colab Pro](https://colab.research.google.com/signup)上进行了所有的速度测试。
| 模型 | 规模<br><sup>(像素) | 准确度<br><sup>第一 | 准确度<br><sup>前五 | 训练<br><sup>90 epochs<br>4xA100 (小时) | 速度<br><sup>ONNX CPU<br>(ms) | 速度<br><sup>TensorRT V100<br>(ms) | 参数<br><sup>(M) | 浮点运算<br><sup>@224 (B) |
|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/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/v6.2/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
<details>
<summary>表格注释 (点击扩展)</summary>
- 所有检查点都被SGD优化器训练到90 epochs, `lr0=0.001` 和 `weight_decay=5e-5`， 图像大小为224，全为默认设置。<br>运行数据记录于 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2。
- **准确度** 值为[ImageNet-1k](https://www.image-net.org/index.php)数据集上的单模型单尺度。<br>通过`python classify/val.py --data ../datasets/imagenet --img 224`进行复制。
- 使用Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM实例得出的100张推理图像的平均**速度**。<br>通过 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`进行复制。
- 用`export.py`**导出**到FP32的ONNX和FP16的TensorRT。<br>通过 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`进行复制。
</details>
</details>
<details>
<summary>分类使用实例 (点击展开)</summary>
### 训练
YOLOv5分类训练支持自动下载MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof和ImageNet数据集，并使用`--data` 参数. 打个比方，在MNIST上使用`--data mnist`开始训练。
```bash
# 单GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
# 多-GPU DDP
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数据集上验证YOLOv5m-cl的准确性:
```bash
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
```
### 预测
用提前训练好的YOLOv5s-cls.pt去预测bus.jpg:
```bash
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
```
```python
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub
```
### 导出
导出一组训练好的YOLOv5s-cls, ResNet和EfficientNet模型到ONNX和TensorRT:
```bash
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
```
</details>
## <div align="center">贡献</div>
我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的 [贡献指南](CONTRIBUTING.md)，填写[YOLOv5调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 来向我们发送您的经验反馈。真诚感谢我们所有的贡献者!
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png" /></a>
## <div align="center">联系</div>
关于YOLOv5的漏洞和功能问题，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。商业咨询或技术支持服务请访问[https://ultralytics.com/contact](https://ultralytics.com/contact)。
<br>
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<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="3%" alt="" /></a>
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</div>
[assets]: https://github.com/ultralytics/yolov5/releases
[tta]: https://github.com/ultralytics/yolov5/issues/303
.github/SECURITY.md 0 → 100644
View file @ cf1ed55a
# Security Policy
We aim to make YOLOv5 🚀 as secure as possible! If you find potential vulnerabilities or have any concerns please let us know so we can investigate and take corrective action if needed.
### Reporting a Vulnerability
To report vulnerabilities please email us at hello@ultralytics.com or visit https://ultralytics.com/contact. Thank you!
.github/dependabot.yml 0 → 100644
View file @ cf1ed55a
version: 2
updates:
- package-ecosystem: pip
directory: "/"
schedule:
interval: weekly
time: "04:00"
open-pull-requests-limit: 10
reviewers:
- glenn-jocher
labels:
- dependencies
- package-ecosystem: github-actions
directory: "/"
schedule:
interval: weekly
time: "04:00"
open-pull-requests-limit: 5
reviewers:
- glenn-jocher
labels:
- dependencies
.github/workflows/ci-testing.yml 0 → 100644
View file @ cf1ed55a
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# YOLOv5 Continuous Integration (CI) GitHub Actions tests
name: YOLOv5 CI
on:
push:
branches: [ master ]
pull_request:
branches: [ master ]
schedule:
- cron: '0 0 * * *' # runs at 00:00 UTC every day
jobs:
Benchmarks:
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ ubuntu-latest ]
python-version: [ '3.9' ] # requires python<=3.9
model: [ yolov5n ]
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
#- name: Cache pip
# uses: actions/cache@v3
# with:
# path: ~/.cache/pip
# key: ${{ runner.os }}-Benchmarks-${{ hashFiles('requirements.txt') }}
# restore-keys: ${{ runner.os }}-Benchmarks-
- name: Install requirements
run: |
python -m pip install --upgrade pip wheel
pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu
python --version
pip --version
pip list
- name: Benchmark DetectionModel
run: |
python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29
- name: Benchmark SegmentationModel
run: |
python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22
- name: Test predictions
run: |
python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224
python detect.py --weights ${{ matrix.model }}.onnx --img 320
python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320
python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224
Tests:
timeout-minutes: 60
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ ubuntu-latest, windows-latest ] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049
python-version: [ '3.10' ]
model: [ yolov5n ]
include:
- os: ubuntu-latest
python-version: '3.7' # '3.6.8' min
model: yolov5n
- os: ubuntu-latest
python-version: '3.8'
model: yolov5n
- os: ubuntu-latest
python-version: '3.9'
model: yolov5n
- os: ubuntu-latest
python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8
model: yolov5n
torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Get cache dir
# https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
id: pip-cache
run: echo "::set-output name=dir::$(pip cache dir)"
- name: Cache pip
uses: actions/cache@v3
with:
path: ${{ steps.pip-cache.outputs.dir }}
key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}
restore-keys: ${{ runner.os }}-${{ matrix.python-version }}-pip-
- name: Install requirements
run: |
python -m pip install --upgrade pip wheel
if [ "${{ matrix.torch }}" == "1.7.0" ]; then
pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu
else
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
fi
shell: bash # for Windows compatibility
- name: Check environment
run: |
python -c "import utils; utils.notebook_init()"
echo "RUNNER_OS is ${{ runner.os }}"
echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
echo "GITHUB_ACTOR is ${{ github.actor }}"
echo "GITHUB_REPOSITORY is ${{ github.repository }}"
echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}"
python --version
pip --version
pip list
- name: Test detection
shell: bash # for Windows compatibility
run: |
# export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories
m=${{ matrix.model }} # official weights
b=runs/train/exp/weights/best # best.pt checkpoint
python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train
for d in cpu; do # devices
for w in $m $b; do # weights
python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val
python detect.py --imgsz 64 --weights $w.pt --device $d # detect
done
done
python hubconf.py --model $m # hub
# python models/tf.py --weights $m.pt # build TF model
python models/yolo.py --cfg $m.yaml # build PyTorch model
python export.py --weights $m.pt --img 64 --include torchscript # export
python - <<EOF
import torch
im = torch.zeros([1, 3, 64, 64])
for path in '$m', '$b':
model = torch.hub.load('.', 'custom', path=path, source='local')
print(model('data/images/bus.jpg'))
model(im) # warmup, build grids for trace
torch.jit.trace(model, [im])
EOF
- name: Test segmentation
shell: bash # for Windows compatibility
run: |
m=${{ matrix.model }}-seg # official weights
b=runs/train-seg/exp/weights/best # best.pt checkpoint
python segment/train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train
python segment/train.py --imgsz 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device cpu # train
for d in cpu; do # devices
for w in $m $b; do # weights
python segment/val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val
python segment/predict.py --imgsz 64 --weights $w.pt --device $d # predict
python export.py --weights $w.pt --img 64 --include torchscript --device $d # export
done
done
- name: Test classification
shell: bash # for Windows compatibility
run: |
m=${{ matrix.model }}-cls.pt # official weights
b=runs/train-cls/exp/weights/best.pt # best.pt checkpoint
python classify/train.py --imgsz 32 --model $m --data mnist160 --epochs 1 # train
python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist160 # val
python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist160/test/7/60.png # predict
python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg # predict
python export.py --weights $b --img 64 --include torchscript # export
python - <<EOF
import torch
for path in '$m', '$b':
model = torch.hub.load('.', 'custom', path=path, source='local')
EOF
.github/workflows/codeql-analysis.yml 0 → 100644
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# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities.
# https://github.com/github/codeql-action
name: "CodeQL"
on:
schedule:
- cron: '0 0 1 * *' # Runs at 00:00 UTC on the 1st of every month
jobs:
analyze:
name: Analyze
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
language: ['python']
# CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
# Learn more:
# https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
steps:
- name: Checkout repository
uses: actions/checkout@v3
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
uses: github/codeql-action/init@v2
with:
languages: ${{ matrix.language }}
# If you wish to specify custom queries, you can do so here or in a config file.
# By default, queries listed here will override any specified in a config file.
# Prefix the list here with "+" to use these queries and those in the config file.
# queries: ./path/to/local/query, your-org/your-repo/queries@main
# Autobuild attempts to build any compiled languages (C/C++, C#, or Java).
# If this step fails, then you should remove it and run the build manually (see below)
- name: Autobuild
uses: github/codeql-action/autobuild@v2
# ℹ️ Command-line programs to run using the OS shell.
# 📚 https://git.io/JvXDl
# ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
# and modify them (or add more) to build your code if your project
# uses a compiled language
#- run: |
# make bootstrap
# make release
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@v2
.github/workflows/docker.yml 0 → 100644
View file @ cf1ed55a
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Builds ultralytics/yolov5:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov5
name: Publish Docker Images
on:
push:
branches: [ master ]
jobs:
docker:
if: github.repository == 'ultralytics/yolov5'
name: Push Docker image to Docker Hub
runs-on: ubuntu-latest
steps:
- name: Checkout repo
uses: actions/checkout@v3
- name: Set up QEMU
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push arm64 image
uses: docker/build-push-action@v3
continue-on-error: true
with:
context: .
platforms: linux/arm64
file: utils/docker/Dockerfile-arm64
push: true
tags: ultralytics/yolov5:latest-arm64
- name: Build and push CPU image
uses: docker/build-push-action@v3
continue-on-error: true
with:
context: .
file: utils/docker/Dockerfile-cpu
push: true
tags: ultralytics/yolov5:latest-cpu
- name: Build and push GPU image
uses: docker/build-push-action@v3
continue-on-error: true
with:
context: .
file: utils/docker/Dockerfile
push: true
tags: ultralytics/yolov5:latest
.github/workflows/greetings.yml 0 → 100644
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
name: Greetings
on:
pull_request_target:
types: [opened]
issues:
types: [opened]
jobs:
greeting:
runs-on: ubuntu-latest
steps:
- uses: actions/first-interaction@v1
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
pr-message: |
👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
- ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**.
- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
issue-message: |
👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov5/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607).
If this is a 🐛 Bug Report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online [W&B logging](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#visualize) if available.
For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.
## Requirements
[**Python>=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started:
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
## Environments
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):
- **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/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <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>
## Status
<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"></a>
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](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
.github/workflows/stale.yml 0 → 100644
View file @ cf1ed55a
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
name: Close stale issues
on:
schedule:
- cron: '0 0 * * *' # Runs at 00:00 UTC every day
jobs:
stale:
runs-on: ubuntu-latest
steps:
- uses: actions/stale@v6
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
stale-issue-message: |
👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources:
- **Wiki** – https://github.com/ultralytics/yolov5/wiki
- **Tutorials** – https://github.com/ultralytics/yolov5#tutorials
- **Docs** – https://docs.ultralytics.com
Access additional [Ultralytics](https://ultralytics.com) ⚡ resources:
- **Ultralytics HUB** – https://ultralytics.com/hub
- **Vision API** – https://ultralytics.com/yolov5
- **About Us** – https://ultralytics.com/about
- **Join Our Team** – https://ultralytics.com/work
- **Contact Us** – https://ultralytics.com/contact
Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!
stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
days-before-issue-stale: 30
days-before-issue-close: 10
days-before-pr-stale: 90
days-before-pr-close: 30
exempt-issue-labels: 'documentation,tutorial,TODO'
operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.
.gitignore 0 → 100644
View file @ cf1ed55a
log/
runs/
*.pt
# 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
*.torchscript
*.tflite
*.h5
*_saved_model/
*_web_model/
*_openvino_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
.pre-commit-config.yaml 0 → 100644
View file @ cf1ed55a
# Define hooks for code formations
# Will be applied on any updated commit files if a user has installed and linked commit hook
default_language_version:
python: python3.8
# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
ci:
autofix_prs: true
autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
autoupdate_schedule: monthly
# submodules: true
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
# - id: end-of-file-fixer
- id: trailing-whitespace
- id: check-case-conflict
- id: check-yaml
- id: check-toml
- id: pretty-format-json
- id: check-docstring-first
- repo: https://github.com/asottile/pyupgrade
rev: v3.2.0
hooks:
- id: pyupgrade
name: Upgrade code
args: [ --py37-plus ]
- repo: https://github.com/PyCQA/isort
rev: 5.10.1
hooks:
- id: isort
name: Sort imports
- repo: https://github.com/pre-commit/mirrors-yapf
rev: v0.32.0
hooks:
- id: yapf
name: YAPF formatting
- repo: https://github.com/executablebooks/mdformat
rev: 0.7.16
hooks:
- id: mdformat
name: MD formatting
additional_dependencies:
- mdformat-gfm
- mdformat-black
exclude: "README.md|README_cn.md"
- repo: https://github.com/asottile/yesqa
rev: v1.4.0
hooks:
- id: yesqa
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
hooks:
- id: flake8
name: PEP8
Arial.ttf 0 → 100644
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File added
CONTRIBUTING.md 0 → 100644
View file @ cf1ed55a
## Contributing to YOLOv5 🚀
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
- Reporting a bug
- Discussing the current state of the code
- Submitting a fix
- Proposing a new feature
- Becoming a maintainer
YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be
helping push the frontiers of what's possible in AI 😃!
## Submitting a Pull Request (PR) 🛠️
Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
### 1. Select File to Update
Select `requirements.txt` to update by clicking on it in GitHub.
<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'
Button is in 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 Changes
Change `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 PR
Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
<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 recommendations
To allow your work to be integrated as seamlessly as possible, we advise you to:
- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update
your code by clicking 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>
- ✅ Verify all YOLOv5 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>
- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"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 spot a problem with YOLOv5 please submit a Bug Report!
For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few
short guidelines below to help users provide what we need in order to get started.
When asking a question, people will be better able to provide help if you provide **code** that they can easily
understand and use to **reproduce** the problem. This is referred to by community members as creating
a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
the problem should be:
- ✅ **Minimal** – Use as little code as possible that still produces the same problem
- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
should be:
- ✅ **Current** – Verify that your code is up-to-date with current
GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new
copy to ensure your problem has not already been resolved by previous commits.
- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛
**Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing
a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
understand and diagnose your problem.
## License
By contributing, you agree that your contributions will be licensed under
the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)
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