"test/vscode:/vscode.git/clone" did not exist on "c2740644ee31d29861d3d9977a43af1e71779054"
Commit 7a650e36 authored by mashun1's avatar mashun1
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yolov5-qat

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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
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*.egg
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# 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
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# Installer logs
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*.cover
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# dotenv
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.venv*
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# 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
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# Thumbnails
._*
# Files that might appear in the root of a volume
.DocumentRevisions-V100
.fseventsd
.Spotlight-V100
.TemporaryItems
.Trashes
.VolumeIcon.icns
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# Directories potentially created on remote AFP share
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Network Trash Folder
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# 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
# this drop notebooks from GitHub language stats
*.ipynb linguist-vendored
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://docs.ultralytics.com/help/minimum_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://docs.ultralytics.com/help/contributing) to get started.
options:
- label: Yes I'd like to help by submitting a PR!
blank_issues_enabled: true
contact_links:
- name: 📄 Docs
url: https://docs.ultralytics.com/yolov5
about: View Ultralytics YOLOv5 Docs
- name: 💬 Forum
url: https://community.ultralytics.com/
about: Ask on Ultralytics Community Forum
- name: 🎧 Discord
url: https://ultralytics.com/discord
about: Ask on Ultralytics Discord
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://docs.ultralytics.com/help/contributing) to get started.
options:
- label: Yes I'd like to help by submitting a PR!
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?
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Dependabot for package version updates
# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
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: "/.github/workflows"
schedule:
interval: weekly
time: "04:00"
open-pull-requests-limit: 5
reviewers:
- glenn-jocher
labels:
- dependencies
# YOLOv5 🚀 by Ultralytics, AGPL-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: ["3.11"] # requires python<=3.10
model: [yolov5n]
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: "pip" # caching pip dependencies
- 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
yolo checks
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: ["3.11"]
model: [yolov5n]
include:
- os: ubuntu-latest
python-version: "3.8" # '3.6.8' min
model: yolov5n
- os: ubuntu-latest
python-version: "3.9"
model: yolov5n
- os: ubuntu-latest
python-version: "3.8" # torch 1.8.0 requires python >=3.6, <=3.8
model: yolov5n
torch: "1.8.0" # min torch version CI https://pypi.org/project/torchvision/
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: "pip" # caching pip dependencies
- name: Install requirements
run: |
python -m pip install --upgrade pip wheel
if [ "${{ matrix.torch }}" == "1.8.0" ]; then
pip install -r requirements.txt torch==1.8.0 torchvision==0.9.0 --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: |
yolo checks
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
Summary:
runs-on: ubuntu-latest
needs: [Benchmarks, Tests] # Add job names that you want to check for failure
if: always() # This ensures the job runs even if previous jobs fail
steps:
- name: Check for failure and notify
if: (needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure' || needs.Benchmarks.result == 'cancelled' || needs.Tests.result == 'cancelled') && github.repository == 'ultralytics/yolov5' && (github.event_name == 'schedule' || github.event_name == 'push')
uses: slackapi/slack-github-action@v1.25.0
with:
payload: |
{"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"}
env:
SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
# 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
workflow_dispatch:
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@v4
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
uses: github/codeql-action/init@v3
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@v3
# 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@v3
# YOLOv5 🚀 by Ultralytics, AGPL-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]
workflow_dispatch:
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@v4
with:
fetch-depth: 0 # copy full .git directory to access full git history in Docker images
- name: Set up QEMU
uses: docker/setup-qemu-action@v3
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push arm64 image
uses: docker/build-push-action@v5
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@v5
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@v5
continue-on-error: true
with:
context: .
file: utils/docker/Dockerfile
push: true
tags: ultralytics/yolov5:latest
# Ultralytics 🚀 - AGPL-3.0 license
# Ultralytics Actions https://github.com/ultralytics/actions
# This workflow automatically formats code and documentation in PRs to official Ultralytics standards
name: Ultralytics Actions
on:
push:
branches: [main, master]
pull_request_target:
branches: [main, master]
jobs:
format:
runs-on: ubuntu-latest
steps:
- name: Run Ultralytics Formatting
uses: ultralytics/actions@main
with:
token: ${{ secrets.GITHUB_TOKEN }} # automatically generated, do not modify
python: true # format Python code and docstrings
markdown: true # format Markdown and YAML
spelling: true # check spelling
links: false # check broken links
summary: true # print PR summary with GPT4 (requires 'openai_api_key' or 'openai_azure_api_key' and 'openai_azure_endpoint')
openai_azure_api_key: ${{ secrets.OPENAI_AZURE_API_KEY }}
openai_azure_endpoint: ${{ secrets.OPENAI_AZURE_ENDPOINT }}
# YOLOv5 🚀 by Ultralytics, AGPL-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://docs.ultralytics.com/yolov5/) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) all the way to advanced concepts like [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/).
If this is a 🐛 Bug Report, please provide a **minimum reproducible example** to help us debug it.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/).
## Requirements
[**Python>=3.8.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](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://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
- **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>
## 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.
## Introducing YOLOv8 🚀
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀!
Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with:
```bash
pip install ultralytics
```
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee
# Ignores the following status codes to reduce false positives:
# - 403(OpenVINO, 'forbidden')
# - 429(Instagram, 'too many requests')
# - 500(Zenodo, 'cached')
# - 502(Zenodo, 'bad gateway')
# - 999(LinkedIn, 'unknown status code')
name: Check Broken links
on:
workflow_dispatch:
schedule:
- cron: "0 0 * * *" # runs at 00:00 UTC every day
jobs:
Links:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Download and install lychee
run: |
LYCHEE_URL=$(curl -s https://api.github.com/repos/lycheeverse/lychee/releases/latest | grep "browser_download_url" | grep "x86_64-unknown-linux-gnu.tar.gz" | cut -d '"' -f 4)
curl -L $LYCHEE_URL -o lychee.tar.gz
tar xzf lychee.tar.gz
sudo mv lychee /usr/local/bin
- name: Test Markdown and HTML links with retry
uses: nick-invision/retry@v3
with:
timeout_minutes: 5
retry_wait_seconds: 60
max_attempts: 3
command: |
lychee \
--scheme 'https' \
--timeout 60 \
--insecure \
--accept 403,429,500,502,999 \
--exclude-all-private \
--exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
--exclude-path '**/ci.yaml' \
--github-token ${{ secrets.GITHUB_TOKEN }} \
'./**/*.md' \
'./**/*.html'
- name: Test Markdown, HTML, YAML, Python and Notebook links with retry
if: github.event_name == 'workflow_dispatch'
uses: nick-invision/retry@v3
with:
timeout_minutes: 5
retry_wait_seconds: 60
max_attempts: 3
command: |
lychee \
--scheme 'https' \
--timeout 60 \
--insecure \
--accept 429,999 \
--exclude-all-private \
--exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
--exclude-path '**/ci.yaml' \
--github-token ${{ secrets.GITHUB_TOKEN }} \
'./**/*.md' \
'./**/*.html' \
'./**/*.yml' \
'./**/*.yaml' \
'./**/*.py' \
'./**/*.ipynb'
# YOLOv5 🚀 by Ultralytics, AGPL-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@v9
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
stale-issue-message: |
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
- **Docs**: https://docs.ultralytics.com
- **HUB**: https://hub.ultralytics.com
- **Community**: https://community.ultralytics.com
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 YOLO 🚀 and Vision AI ⭐
stale-pr-message: |
👋 Hello there! We wanted to let you know that we've decided to close this pull request due to inactivity. We appreciate the effort you put into contributing to our project, but unfortunately, not all contributions are suitable or aligned with our product roadmap.
We hope you understand our decision, and please don't let it discourage you from contributing to open source projects in the future. We value all of our community members and their contributions, and we encourage you to keep exploring new projects and ways to get involved.
For additional resources and information, please see the links below:
- **Docs**: https://docs.ultralytics.com
- **HUB**: https://hub.ultralytics.com
- **Community**: https://community.ultralytics.com
Thank you for your contributions to YOLO 🚀 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.
# 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/
*_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
*.onnx
*.pt
*.pth
*.trt
nohup*.txt
checkpoints/
run/
\ No newline at end of file
This diff is collapsed.
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"
## 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'
The button is 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 Changes
Change 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 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 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://docs.ultralytics.com/help/minimum_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 the 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 provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and 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/)
This diff is collapsed.
# Yolov5-QAT
本项目旨在对Yolov5模型执行量化感知训练,将其转换为onnx模型,并在TensorRT上运行。
## 论文
## 模型结构
yolov5模型由三个部分组成,分别是`Backbone``Neck`以及`Head`。其中`Backbone`网络用于获取不同大小的特征图,`Neck`
融合这些特征,最终生成三个特征图P3、P4和P5(在YOLOv5中,维度用80×80、40×40和20×20表示),分别用于检测图片中的小、中、大物体,`Head`对特征图中的每个像素使用预设的先验锚点进行置信度计算和边界框回归,从而得到一个包含目标类别、类别置信度、边界框坐标、宽度和高度信息的多维数组(BBoxes)。
![alt text](readme_imgs/image-1.png)
## 算法原理
YOLOv5 是一种基于单阶段目标检测算法,通过将图像划分为不同大小的网格,预测每个网格中的目标类别和边界框,利用特征金字塔结构和自适应的模型缩放来实现高效准确的实时目标检测。
![alt text](readme_imgs/image-2.png)
## 环境配置
### Anaconda (方法一)
1、本项目目前仅支持在N卡环境运行
python 3.9.18
torch 2.0.1
cuda 11
pip install -r requirements.txt
pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com pytorch-quantization
2、TensorRT
wget https://github.com/NVIDIA/TensorRT/archive/refs/tags/8.5.3.zip
unzip [下载的压缩包] -d [解压路径]
pip install 解压路径/python/tensorrt-8.5.3.1-cp39-none-linux_x86_64.whl
ln -s 解压路径(绝对路径)/bin/trtexec /usr/local/bin/trtexec
注意:若需要`cu12`则将`requirements.txt`中的相关注释关闭,并安装。
## 数据集
本项目使用coco2017数据集.
数据结构如下
images
├── train2017s
├── val2017
labels
├── train2017
├── val2017
annotations
├── instances_train2017.json
├── instances_val2017.json
train2017.txt
val2017.txt
其中`train2017.txt, val2017.txt`可运行脚本`scripts/coco2yolo.py`生成,
python scripts/coco2yolo.py \
--json_path=path/to/instances_[val/train]2017.json \
--save_path=path/to/labels/[val/train]2017.txt
## 训练
# 训练QAT模型
python scripts/qat.py quantize yolov5[s|m|n|x|l].pt \
--qat=/path/to/save/qat/model --cocodir=/path/to/train/data --eval-origin
# 导出 onnx 模型
python scripts/qat.py export /path/to/load/qat/model --size=640 --save=/path/to/save/onnx/model
# 获取tensorrt模型
trtexec --onnx=/path/to/load/onnx/model --saveEngine=/path/to/save/trt/model --int8
注意:更多选项参考`参考资料部分`
## 推理
python evaluate.py --data_list=/path/to/load/trainlist --label_dir=/path/to/labeldir
--weight=/path/to/load/model --mtype=[ori|onnx|trt|qat]
注意:mtype和weight类型需要严格对应,ori表示原始模型,qat表示基于原始模型得到的qat模型。
## result
![alt text](readme_imgs/trt.png)
### 精度
平台:A800
精度:FP32 + INT8
|模型名称|模型类型|size|map@50-95|map@50|推理速度
|:---|:---|:---|:---|:---|:---|
|yolov5n|原始 <br> ONNX <br> TRT|640|0.276 <br> 0.267 <br> 0.267 | 0.453 <br> 0.444 <br> 0.443 | 8.98ms/sample <br> 9.9ms/sample <br> 3.9ms/sample|
|yolov5s|原始 <br> ONNX <br> TRT|640| 0.369 <br> 0.361 <br> 0.362| 0.562 <br> 0.557 <br> 0.557| 10.0ms/sample <br> 11.4ms/sample <br> 3.74ms/sample|
|yolov5m|原始 <br> ONNX <br> TRT|640|0.445 <br> 0.437 <br> 0.438| 0.633 <br> 0.628 <br> 0.628| 12.8ms/sample <br> 13.2ms/sample <br> 4.4ms/sample|
|yolov5l|原始 <br> ONNX <br> TRT|640|0.482 <br> 0.473 <br> 0.473| 0.663 <br> 0.659 <br> 0.659| 15.6ms/sample <br> 17ms/sample <br> 4.73ms/sample|
|yolov5x|原始 <br> ONNX <br> TRT|640|0.498 <br> 0.492 <br> 0.491| 0.679 <br> 0.676 <br> 0.675| 18ms/sample <br> 20.2ms/sample <br> 5.62ms/sample|
注意:该评测结果不代表模型的真正结果,仅展示不同模型之间的指标差异。
## 应用场景
### 算法类别
`目标检测`
### 热点应用行业
`金融,交通,教育`
## 源码仓库及问题反馈
* https://developer.hpccube.com/codes/modelzoo/vgg16-qat_pytorch
## 参考资料
* https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/index.html
* https://github.com/NVIDIA-AI-IOT/cuDLA-samples/
* https://github.com/ultralytics/yolov5
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