Delete yolov5

yolov5/.dockerignore

@@ -1,222 +0,0 @@
-# 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

yolov5/.gitattributes

@@ -1,2 +0,0 @@
-# this drop notebooks from GitHub language stats
-*.ipynb linguist-vendored

yolov5/.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,87 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-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!

yolov5/.github/ISSUE_TEMPLATE/config.yml

@@ -1,13 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-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

yolov5/.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,52 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-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!

yolov5/.github/ISSUE_TEMPLATE/question.yml

@@ -1,35 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-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?

yolov5/.github/dependabot.yml

@@ -1,28 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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/.github/workflows/ci-testing.yml

@@ -1,151 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
-  workflow_dispatch:
-
-jobs:
-  Benchmarks:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: false
-      matrix:
-        os: [ubuntu-latest]
-        python-version: ["3.11"] # requires python<=3.11
-        model: [yolov5n]
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-          cache: "pip" # cache pip dependencies
-      - name: Install requirements
-        run: |
-          python -m pip install --upgrade pip wheel
-          pip install -r requirements.txt coremltools openvino-dev "tensorflow-cpu<2.15.1" --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-14] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049
-        python-version: ["3.11"]
-        model: [yolov5n]
-        include:
-          - 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: ${{ matrix.python-version }}
-          cache: "pip" # caching pip dependencies
-      - name: Install requirements
-        run: |
-          python -m pip install --upgrade pip wheel
-          torch=""
-          if [ "${{ matrix.torch }}" == "1.8.0" ]; then
-            torch="torch==1.8.0 torchvision==0.9.0"
-          fi
-          pip install -r requirements.txt $torch --extra-index-url https://download.pytorch.org/whl/cpu
-        shell: bash # for Windows compatibility
-      - name: Check environment
-        run: |
-          yolo checks
-          pip list
-      - name: Test detection
-        shell: bash # for Windows compatibility
-        run: |
-          # export PYTHONPATH="$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]
-    if: always()
-    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') && github.run_attempt == '1'
-        uses: slackapi/[email protected]
-        with:
-          webhook-type: incoming-webhook
-          webhook: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}
-          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"

yolov5/.github/workflows/cla.yml

@@ -1,45 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA
-# This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged
-
-name: CLA Assistant
-on:
-  issue_comment:
-    types:
-      - created
-  pull_request_target:
-    types:
-      - reopened
-      - opened
-      - synchronize
-
-permissions:
-  actions: write
-  contents: write
-  pull-requests: write
-  statuses: write
-
-jobs:
-  CLA:
-    if: github.repository == 'ultralytics/yolov5'
-    runs-on: ubuntu-latest
-    steps:
-      - name: CLA Assistant
-        if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'
-        uses: contributor-assistant/[email protected]
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          # Must be repository secret PAT
-          PERSONAL_ACCESS_TOKEN: ${{ secrets._GITHUB_TOKEN }}
-        with:
-          path-to-signatures: "signatures/version1/cla.json"
-          path-to-document: "https://docs.ultralytics.com/help/CLA" # CLA document
-          # Branch must not be protected
-          branch: "cla-signatures"
-          allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot*
-
-          remote-organization-name: ultralytics
-          remote-repository-name: cla
-          custom-pr-sign-comment: "I have read the CLA Document and I sign the CLA"
-          custom-allsigned-prcomment: All Contributors have signed the CLA. ✅

yolov5/.github/workflows/docker.yml

@@ -1,61 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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@v6
-        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@v6
-        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@v6
-        continue-on-error: true
-        with:
-          context: .
-          file: utils/docker/Dockerfile
-          push: true
-          tags: ultralytics/yolov5:latest

yolov5/.github/workflows/format.yml

@@ -1,59 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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:
-  issues:
-    types: [opened]
-  pull_request:
-    branches: [main, master]
-    types: [opened, closed, synchronize, review_requested]
-
-jobs:
-  format:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Run Ultralytics Formatting
-        uses: ultralytics/actions@main
-        with:
-          token: ${{ secrets._GITHUB_TOKEN || secrets.GITHUB_TOKEN }}
-          labels: true # autolabel issues and PRs
-          python: true # format Python code and docstrings
-          prettier: true # format YAML, JSON, Markdown and CSS
-          spelling: true # check spelling
-          links: false # check broken links
-          summary: true # print PR summary with GPT4o (requires 'openai_api_key')
-          openai_api_key: ${{ secrets.OPENAI_API_KEY }}
-          first_issue_response: |
-            👋 Hello @${{ 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/guides/model-training-tips/).
-
-            ## 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/models/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.

yolov5/.github/workflows/links.yml

@@ -1,72 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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 | tar xz -C /usr/local/bin
-
-      - name: Test Markdown and HTML links with retry
-        uses: ultralytics/actions/retry@main
-        with:
-          timeout_minutes: 5
-          retry_delay_seconds: 60
-          retries: 2
-          run: |
-            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 }} \
-            --header "User-Agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36" \
-            './**/*.md' \
-            './**/*.html' | tee -a $GITHUB_STEP_SUMMARY
-
-      - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
-        if: github.event_name == 'workflow_dispatch'
-        uses: ultralytics/actions/retry@main
-        with:
-          timeout_minutes: 5
-          retry_delay_seconds: 60
-          retries: 2
-          run: |
-            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 }} \
-            --header "User-Agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36" \
-            './**/*.md' \
-            './**/*.html' \
-            './**/*.yml' \
-            './**/*.yaml' \
-            './**/*.py' \
-            './**/*.ipynb' | tee -a $GITHUB_STEP_SUMMARY

yolov5/.github/workflows/merge-main-into-prs.yml

@@ -1,72 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Automatically merges repository 'main' branch into all open PRs to keep them up-to-date
-# Action runs on updates to main branch so when one PR merges to main all others update
-
-name: Merge main into PRs
-
-on:
-  workflow_dispatch:
-  # push:
-  #   branches:
-  #     - ${{ github.event.repository.default_branch }}
-
-jobs:
-  Merge:
-    if: github.repository == 'ultralytics/yolov5'
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.x"
-          cache: "pip"
-      - name: Install requirements
-        run: |
-          pip install pygithub
-      - name: Merge default branch into PRs
-        shell: python
-        run: |
-          from github import Github
-          import os
-
-          g = Github(os.getenv('GITHUB_TOKEN'))
-          repo = g.get_repo(os.getenv('GITHUB_REPOSITORY'))
-
-          # Fetch the default branch name
-          default_branch_name = repo.default_branch
-          default_branch = repo.get_branch(default_branch_name)
-
-          for pr in repo.get_pulls(state='open', sort='created'):
-              try:
-                  # Get full names for repositories and branches
-                  base_repo_name = repo.full_name
-                  head_repo_name = pr.head.repo.full_name
-                  base_branch_name = pr.base.ref
-                  head_branch_name = pr.head.ref
-
-                  # Check if PR is behind the default branch
-                  comparison = repo.compare(default_branch.commit.sha, pr.head.sha)
-                  
-                  if comparison.behind_by > 0:
-                      print(f"⚠️ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is behind {default_branch_name} by {comparison.behind_by} commit(s).")
-                      
-                      # Attempt to update the branch
-                      try:
-                          success = pr.update_branch()
-                          assert success, "Branch update failed"
-                          print(f"✅ Successfully merged '{default_branch_name}' into PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}).")
-                      except Exception as update_error:
-                          print(f"❌ Could not update PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}): {update_error}")
-                          print("   This might be due to branch protection rules or insufficient permissions.")
-                  else:
-                      print(f"✅ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is up to date with {default_branch_name}.")
-              except Exception as e:
-                  print(f"❌ Could not process PR #{pr.number}: {e}")
-
-        env:
-          GITHUB_TOKEN: ${{ secrets._GITHUB_TOKEN }}
-          GITHUB_REPOSITORY: ${{ github.repository }}

yolov5/.github/workflows/stale.yml

@@ -1,47 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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.

yolov5/.gitignore

@@ -1,258 +0,0 @@
-# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
-*.jpg
-*.jpeg
-*.png
-*.bmp
-*.tif
-*.tiff
-*.heic
-*.JPG
-*.JPEG
-*.PNG
-*.BMP
-*.TIF
-*.TIFF
-*.HEIC
-*.mp4
-*.mov
-*.MOV
-*.avi
-*.data
-*.json
-*.cfg
-!setup.cfg
-!cfg/yolov3*.cfg
-
-storage.googleapis.com
-runs/*
-data/*
-data/images/*
-!data/*.yaml
-!data/hyps
-!data/scripts
-!data/images
-!data/images/zidane.jpg
-!data/images/bus.jpg
-!data/*.sh
-
-results*.csv
-
-# Datasets -------------------------------------------------------------------------------------------------------------
-coco/
-coco128/
-VOC/
-
-# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
-*.m~
-*.mat
-!targets*.mat
-
-# Neural Network weights -----------------------------------------------------------------------------------------------
-*.weights
-*.pt
-*.pb
-*.onnx
-*.engine
-*.mlmodel
-*.mlpackage
-*.torchscript
-*.tflite
-*.h5
-*_saved_model/
-*_web_model/
-*_openvino_model/
-*_paddle_model/
-darknet53.conv.74
-yolov3-tiny.conv.15
-
-# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-/wandb/
-.installed.cfg
-*.egg
-
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# dotenv
-.env
-
-# virtualenv
-.venv*
-venv*/
-ENV*/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-
-# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
-
-# General
-.DS_Store
-.AppleDouble
-.LSOverride
-
-# Icon must end with two \r
-Icon
-Icon?
-
-# Thumbnails
-._*
-
-# Files that might appear in the root of a volume
-.DocumentRevisions-V100
-.fseventsd
-.Spotlight-V100
-.TemporaryItems
-.Trashes
-.VolumeIcon.icns
-.com.apple.timemachine.donotpresent
-
-# Directories potentially created on remote AFP share
-.AppleDB
-.AppleDesktop
-Network Trash Folder
-Temporary Items
-.apdisk
-
-
-# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
-# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
-# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
-
-# User-specific stuff:
-.idea/*
-.idea/**/workspace.xml
-.idea/**/tasks.xml
-.idea/dictionaries
-.html  # Bokeh Plots
-.pg  # TensorFlow Frozen Graphs
-.avi # videos
-
-# Sensitive or high-churn files:
-.idea/**/dataSources/
-.idea/**/dataSources.ids
-.idea/**/dataSources.local.xml
-.idea/**/sqlDataSources.xml
-.idea/**/dynamic.xml
-.idea/**/uiDesigner.xml
-
-# Gradle:
-.idea/**/gradle.xml
-.idea/**/libraries
-
-# CMake
-cmake-build-debug/
-cmake-build-release/
-
-# Mongo Explorer plugin:
-.idea/**/mongoSettings.xml
-
-## File-based project format:
-*.iws
-
-## Plugin-specific files:
-
-# IntelliJ
-out/
-
-# mpeltonen/sbt-idea plugin
-.idea_modules/
-
-# JIRA plugin
-atlassian-ide-plugin.xml
-
-# Cursive Clojure plugin
-.idea/replstate.xml
-
-# Crashlytics plugin (for Android Studio and IntelliJ)
-com_crashlytics_export_strings.xml
-crashlytics.properties
-crashlytics-build.properties
-fabric.properties

yolov5/CITATION.cff

@@ -1,14 +0,0 @@
-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"

yolov5/CONTRIBUTING.md

@@ -1,76 +0,0 @@
-## 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://www.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://www.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/)

yolov5/LICENSE

@@ -1,661 +0,0 @@
-                    GNU AFFERO GENERAL PUBLIC LICENSE
-                       Version 3, 19 November 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU Affero General Public License is a free, copyleft license for
-software and other kinds of works, specifically designed to ensure
-cooperation with the community in the case of network server software.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-our General Public Licenses are intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  Developers that use our General Public Licenses protect your rights
-with two steps: (1) assert copyright on the software, and (2) offer
-you this License which gives you legal permission to copy, distribute
-and/or modify the software.
-
-  A secondary benefit of defending all users' freedom is that
-improvements made in alternate versions of the program, if they
-receive widespread use, become available for other developers to
-incorporate.  Many developers of free software are heartened and
-encouraged by the resulting cooperation.  However, in the case of
-software used on network servers, this result may fail to come about.
-The GNU General Public License permits making a modified version and
-letting the public access it on a server without ever releasing its
-source code to the public.
-
-  The GNU Affero General Public License is designed specifically to
-ensure that, in such cases, the modified source code becomes available
-to the community.  It requires the operator of a network server to
-provide the source code of the modified version running there to the
-users of that server.  Therefore, public use of a modified version, on
-a publicly accessible server, gives the public access to the source
-code of the modified version.
-
-  An older license, called the Affero General Public License and
-published by Affero, was designed to accomplish similar goals.  This is
-a different license, not a version of the Affero GPL, but Affero has
-released a new version of the Affero GPL which permits relicensing under
-this license.
-
-  The precise terms and conditions for copying, distribution and
-modification follow.
-
-                       TERMS AND CONDITIONS
-
-  0. Definitions.
-
-  "This License" refers to version 3 of the GNU Affero General Public License.
-
-  "Copyright" also means copyright-like laws that apply to other kinds of
-works, such as semiconductor masks.
-
-  "The Program" refers to any copyrightable work licensed under this
-License.  Each licensee is addressed as "you".  "Licensees" and
-"recipients" may be individuals or organizations.
-
-  To "modify" a work means to copy from or adapt all or part of the work
-in a fashion requiring copyright permission, other than the making of an
-exact copy.  The resulting work is called a "modified version" of the
-earlier work or a work "based on" the earlier work.
-
-  A "covered work" means either the unmodified Program or a work based
-on the Program.
-
-  To "propagate" a work means to do anything with it that, without
-permission, would make you directly or secondarily liable for
-infringement under applicable copyright law, except executing it on a
-computer or modifying a private copy.  Propagation includes copying,
-distribution (with or without modification), making available to the
-public, and in some countries other activities as well.
-
-  To "convey" a work means any kind of propagation that enables other
-parties to make or receive copies.  Mere interaction with a user through
-a computer network, with no transfer of a copy, is not conveying.
-
-  An interactive user interface displays "Appropriate Legal Notices"
-to the extent that it includes a convenient and prominently visible
-feature that (1) displays an appropriate copyright notice, and (2)
-tells the user that there is no warranty for the work (except to the
-extent that warranties are provided), that licensees may convey the
-work under this License, and how to view a copy of this License.  If
-the interface presents a list of user commands or options, such as a
-menu, a prominent item in the list meets this criterion.
-
-  1. Source Code.
-
-  The "source code" for a work means the preferred form of the work
-for making modifications to it.  "Object code" means any non-source
-form of a work.
-
-  A "Standard Interface" means an interface that either is an official
-standard defined by a recognized standards body, or, in the case of
-interfaces specified for a particular programming language, one that
-is widely used among developers working in that language.
-
-  The "System Libraries" of an executable work include anything, other
-than the work as a whole, that (a) is included in the normal form of
-packaging a Major Component, but which is not part of that Major
-Component, and (b) serves only to enable use of the work with that
-Major Component, or to implement a Standard Interface for which an
-implementation is available to the public in source code form.  A
-"Major Component", in this context, means a major essential component
-(kernel, window system, and so on) of the specific operating system
-(if any) on which the executable work runs, or a compiler used to
-produce the work, or an object code interpreter used to run it.
-
-  The "Corresponding Source" for a work in object code form means all
-the source code needed to generate, install, and (for an executable
-work) run the object code and to modify the work, including scripts to
-control those activities.  However, it does not include the work's
-System Libraries, or general-purpose tools or generally available free
-programs which are used unmodified in performing those activities but
-which are not part of the work.  For example, Corresponding Source
-includes interface definition files associated with source files for
-the work, and the source code for shared libraries and dynamically
-linked subprograms that the work is specifically designed to require,
-such as by intimate data communication or control flow between those
-subprograms and other parts of the work.
-
-  The Corresponding Source need not include anything that users
-can regenerate automatically from other parts of the Corresponding
-Source.
-
-  The Corresponding Source for a work in source code form is that
-same work.
-
-  2. Basic Permissions.
-
-  All rights granted under this License are granted for the term of
-copyright on the Program, and are irrevocable provided the stated
-conditions are met.  This License explicitly affirms your unlimited
-permission to run the unmodified Program.  The output from running a
-covered work is covered by this License only if the output, given its
-content, constitutes a covered work.  This License acknowledges your
-rights of fair use or other equivalent, as provided by copyright law.
-
-  You may make, run and propagate covered works that you do not
-convey, without conditions so long as your license otherwise remains
-in force.  You may convey covered works to others for the sole purpose
-of having them make modifications exclusively for you, or provide you
-with facilities for running those works, provided that you comply with
-the terms of this License in conveying all material for which you do
-not control copyright.  Those thus making or running the covered works
-for you must do so exclusively on your behalf, under your direction
-and control, on terms that prohibit them from making any copies of
-your copyrighted material outside their relationship with you.
-
-  Conveying under any other circumstances is permitted solely under
-the conditions stated below.  Sublicensing is not allowed; section 10
-makes it unnecessary.
-
-  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
-
-  No covered work shall be deemed part of an effective technological
-measure under any applicable law fulfilling obligations under article
-11 of the WIPO copyright treaty adopted on 20 December 1996, or
-similar laws prohibiting or restricting circumvention of such
-measures.
-
-  When you convey a covered work, you waive any legal power to forbid
-circumvention of technological measures to the extent such circumvention
-is effected by exercising rights under this License with respect to
-the covered work, and you disclaim any intention to limit operation or
-modification of the work as a means of enforcing, against the work's
-users, your or third parties' legal rights to forbid circumvention of
-technological measures.
-
-  4. Conveying Verbatim Copies.
-
-  You may convey verbatim copies of the Program's source code as you
-receive it, in any medium, provided that you conspicuously and
-appropriately publish on each copy an appropriate copyright notice;
-keep intact all notices stating that this License and any
-non-permissive terms added in accord with section 7 apply to the code;
-keep intact all notices of the absence of any warranty; and give all
-recipients a copy of this License along with the Program.
-
-  You may charge any price or no price for each copy that you convey,
-and you may offer support or warranty protection for a fee.
-
-  5. Conveying Modified Source Versions.
-
-  You may convey a work based on the Program, or the modifications to
-produce it from the Program, in the form of source code under the
-terms of section 4, provided that you also meet all of these conditions:
-
-    a) The work must carry prominent notices stating that you modified
-    it, and giving a relevant date.
-
-    b) The work must carry prominent notices stating that it is
-    released under this License and any conditions added under section
-    7.  This requirement modifies the requirement in section 4 to
-    "keep intact all notices".
-
-    c) You must license the entire work, as a whole, under this
-    License to anyone who comes into possession of a copy.  This
-    License will therefore apply, along with any applicable section 7
-    additional terms, to the whole of the work, and all its parts,
-    regardless of how they are packaged.  This License gives no
-    permission to license the work in any other way, but it does not
-    invalidate such permission if you have separately received it.
-
-    d) If the work has interactive user interfaces, each must display
-    Appropriate Legal Notices; however, if the Program has interactive
-    interfaces that do not display Appropriate Legal Notices, your
-    work need not make them do so.
-
-  A compilation of a covered work with other separate and independent
-works, which are not by their nature extensions of the covered work,
-and which are not combined with it such as to form a larger program,
-in or on a volume of a storage or distribution medium, is called an
-"aggregate" if the compilation and its resulting copyright are not
-used to limit the access or legal rights of the compilation's users
-beyond what the individual works permit.  Inclusion of a covered work
-in an aggregate does not cause this License to apply to the other
-parts of the aggregate.
-
-  6. Conveying Non-Source Forms.
-
-  You may convey a covered work in object code form under the terms
-of sections 4 and 5, provided that you also convey the
-machine-readable Corresponding Source under the terms of this License,
-in one of these ways:
-
-    a) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by the
-    Corresponding Source fixed on a durable physical medium
-    customarily used for software interchange.
-
-    b) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by a
-    written offer, valid for at least three years and valid for as
-    long as you offer spare parts or customer support for that product
-    model, to give anyone who possesses the object code either (1) a
-    copy of the Corresponding Source for all the software in the
-    product that is covered by this License, on a durable physical
-    medium customarily used for software interchange, for a price no
-    more than your reasonable cost of physically performing this
-    conveying of source, or (2) access to copy the
-    Corresponding Source from a network server at no charge.
-
-    c) Convey individual copies of the object code with a copy of the
-    written offer to provide the Corresponding Source.  This
-    alternative is allowed only occasionally and noncommercially, and
-    only if you received the object code with such an offer, in accord
-    with subsection 6b.
-
-    d) Convey the object code by offering access from a designated
-    place (gratis or for a charge), and offer equivalent access to the
-    Corresponding Source in the same way through the same place at no
-    further charge.  You need not require recipients to copy the
-    Corresponding Source along with the object code.  If the place to
-    copy the object code is a network server, the Corresponding Source
-    may be on a different server (operated by you or a third party)
-    that supports equivalent copying facilities, provided you maintain
-    clear directions next to the object code saying where to find the
-    Corresponding Source.  Regardless of what server hosts the
-    Corresponding Source, you remain obligated to ensure that it is
-    available for as long as needed to satisfy these requirements.
-
-    e) Convey the object code using peer-to-peer transmission, provided
-    you inform other peers where the object code and Corresponding
-    Source of the work are being offered to the general public at no
-    charge under subsection 6d.
-
-  A separable portion of the object code, whose source code is excluded
-from the Corresponding Source as a System Library, need not be
-included in conveying the object code work.
-
-  A "User Product" is either (1) a "consumer product", which means any
-tangible personal property which is normally used for personal, family,
-or household purposes, or (2) anything designed or sold for incorporation
-into a dwelling.  In determining whether a product is a consumer product,
-doubtful cases shall be resolved in favor of coverage.  For a particular
-product received by a particular user, "normally used" refers to a
-typical or common use of that class of product, regardless of the status
-of the particular user or of the way in which the particular user
-actually uses, or expects or is expected to use, the product.  A product
-is a consumer product regardless of whether the product has substantial
-commercial, industrial or non-consumer uses, unless such uses represent
-the only significant mode of use of the product.
-
-  "Installation Information" for a User Product means any methods,
-procedures, authorization keys, or other information required to install
-and execute modified versions of a covered work in that User Product from
-a modified version of its Corresponding Source.  The information must
-suffice to ensure that the continued functioning of the modified object
-code is in no case prevented or interfered with solely because
-modification has been made.
-
-  If you convey an object code work under this section in, or with, or
-specifically for use in, a User Product, and the conveying occurs as
-part of a transaction in which the right of possession and use of the
-User Product is transferred to the recipient in perpetuity or for a
-fixed term (regardless of how the transaction is characterized), the
-Corresponding Source conveyed under this section must be accompanied
-by the Installation Information.  But this requirement does not apply
-if neither you nor any third party retains the ability to install
-modified object code on the User Product (for example, the work has
-been installed in ROM).
-
-  The requirement to provide Installation Information does not include a
-requirement to continue to provide support service, warranty, or updates
-for a work that has been modified or installed by the recipient, or for
-the User Product in which it has been modified or installed.  Access to a
-network may be denied when the modification itself materially and
-adversely affects the operation of the network or violates the rules and
-protocols for communication across the network.
-
-  Corresponding Source conveyed, and Installation Information provided,
-in accord with this section must be in a format that is publicly
-documented (and with an implementation available to the public in
-source code form), and must require no special password or key for
-unpacking, reading or copying.
-
-  7. Additional Terms.
-
-  "Additional permissions" are terms that supplement the terms of this
-License by making exceptions from one or more of its conditions.
-Additional permissions that are applicable to the entire Program shall
-be treated as though they were included in this License, to the extent
-that they are valid under applicable law.  If additional permissions
-apply only to part of the Program, that part may be used separately
-under those permissions, but the entire Program remains governed by
-this License without regard to the additional permissions.
-
-  When you convey a copy of a covered work, you may at your option
-remove any additional permissions from that copy, or from any part of
-it.  (Additional permissions may be written to require their own
-removal in certain cases when you modify the work.)  You may place
-additional permissions on material, added by you to a covered work,
-for which you have or can give appropriate copyright permission.
-
-  Notwithstanding any other provision of this License, for material you
-add to a covered work, you may (if authorized by the copyright holders of
-that material) supplement the terms of this License with terms:
-
-    a) Disclaiming warranty or limiting liability differently from the
-    terms of sections 15 and 16 of this License; or
-
-    b) Requiring preservation of specified reasonable legal notices or
-    author attributions in that material or in the Appropriate Legal
-    Notices displayed by works containing it; or
-
-    c) Prohibiting misrepresentation of the origin of that material, or
-    requiring that modified versions of such material be marked in
-    reasonable ways as different from the original version; or
-
-    d) Limiting the use for publicity purposes of names of licensors or
-    authors of the material; or
-
-    e) Declining to grant rights under trademark law for use of some
-    trade names, trademarks, or service marks; or
-
-    f) Requiring indemnification of licensors and authors of that
-    material by anyone who conveys the material (or modified versions of
-    it) with contractual assumptions of liability to the recipient, for
-    any liability that these contractual assumptions directly impose on
-    those licensors and authors.
-
-  All other non-permissive additional terms are considered "further
-restrictions" within the meaning of section 10.  If the Program as you
-received it, or any part of it, contains a notice stating that it is
-governed by this License along with a term that is a further
-restriction, you may remove that term.  If a license document contains
-a further restriction but permits relicensing or conveying under this
-License, you may add to a covered work material governed by the terms
-of that license document, provided that the further restriction does
-not survive such relicensing or conveying.
-
-  If you add terms to a covered work in accord with this section, you
-must place, in the relevant source files, a statement of the
-additional terms that apply to those files, or a notice indicating
-where to find the applicable terms.
-
-  Additional terms, permissive or non-permissive, may be stated in the
-form of a separately written license, or stated as exceptions;
-the above requirements apply either way.
-
-  8. Termination.
-
-  You may not propagate or modify a covered work except as expressly
-provided under this License.  Any attempt otherwise to propagate or
-modify it is void, and will automatically terminate your rights under
-this License (including any patent licenses granted under the third
-paragraph of section 11).
-
-  However, if you cease all violation of this License, then your
-license from a particular copyright holder is reinstated (a)
-provisionally, unless and until the copyright holder explicitly and
-finally terminates your license, and (b) permanently, if the copyright
-holder fails to notify you of the violation by some reasonable means
-prior to 60 days after the cessation.
-
-  Moreover, your license from a particular copyright holder is
-reinstated permanently if the copyright holder notifies you of the
-violation by some reasonable means, this is the first time you have
-received notice of violation of this License (for any work) from that
-copyright holder, and you cure the violation prior to 30 days after
-your receipt of the notice.
-
-  Termination of your rights under this section does not terminate the
-licenses of parties who have received copies or rights from you under
-this License.  If your rights have been terminated and not permanently
-reinstated, you do not qualify to receive new licenses for the same
-material under section 10.
-
-  9. Acceptance Not Required for Having Copies.
-
-  You are not required to accept this License in order to receive or
-run a copy of the Program.  Ancillary propagation of a covered work
-occurring solely as a consequence of using peer-to-peer transmission
-to receive a copy likewise does not require acceptance.  However,
-nothing other than this License grants you permission to propagate or
-modify any covered work.  These actions infringe copyright if you do
-not accept this License.  Therefore, by modifying or propagating a
-covered work, you indicate your acceptance of this License to do so.
-
-  10. Automatic Licensing of Downstream Recipients.
-
-  Each time you convey a covered work, the recipient automatically
-receives a license from the original licensors, to run, modify and
-propagate that work, subject to this License.  You are not responsible
-for enforcing compliance by third parties with this License.
-
-  An "entity transaction" is a transaction transferring control of an
-organization, or substantially all assets of one, or subdividing an
-organization, or merging organizations.  If propagation of a covered
-work results from an entity transaction, each party to that
-transaction who receives a copy of the work also receives whatever
-licenses to the work the party's predecessor in interest had or could
-give under the previous paragraph, plus a right to possession of the
-Corresponding Source of the work from the predecessor in interest, if
-the predecessor has it or can get it with reasonable efforts.
-
-  You may not impose any further restrictions on the exercise of the
-rights granted or affirmed under this License.  For example, you may
-not impose a license fee, royalty, or other charge for exercise of
-rights granted under this License, and you may not initiate litigation
-(including a cross-claim or counterclaim in a lawsuit) alleging that
-any patent claim is infringed by making, using, selling, offering for
-sale, or importing the Program or any portion of it.
-
-  11. Patents.
-
-  A "contributor" is a copyright holder who authorizes use under this
-License of the Program or a work on which the Program is based.  The
-work thus licensed is called the contributor's "contributor version".
-
-  A contributor's "essential patent claims" are all patent claims
-owned or controlled by the contributor, whether already acquired or
-hereafter acquired, that would be infringed by some manner, permitted
-by this License, of making, using, or selling its contributor version,
-but do not include claims that would be infringed only as a
-consequence of further modification of the contributor version.  For
-purposes of this definition, "control" includes the right to grant
-patent sublicenses in a manner consistent with the requirements of
-this License.
-
-  Each contributor grants you a non-exclusive, worldwide, royalty-free
-patent license under the contributor's essential patent claims, to
-make, use, sell, offer for sale, import and otherwise run, modify and
-propagate the contents of its contributor version.
-
-  In the following three paragraphs, a "patent license" is any express
-agreement or commitment, however denominated, not to enforce a patent
-(such as an express permission to practice a patent or covenant not to
-sue for patent infringement).  To "grant" such a patent license to a
-party means to make such an agreement or commitment not to enforce a
-patent against the party.
-
-  If you convey a covered work, knowingly relying on a patent license,
-and the Corresponding Source of the work is not available for anyone
-to copy, free of charge and under the terms of this License, through a
-publicly available network server or other readily accessible means,
-then you must either (1) cause the Corresponding Source to be so
-available, or (2) arrange to deprive yourself of the benefit of the
-patent license for this particular work, or (3) arrange, in a manner
-consistent with the requirements of this License, to extend the patent
-license to downstream recipients.  "Knowingly relying" means you have
-actual knowledge that, but for the patent license, your conveying the
-covered work in a country, or your recipient's use of the covered work
-in a country, would infringe one or more identifiable patents in that
-country that you have reason to believe are valid.
-
-  If, pursuant to or in connection with a single transaction or
-arrangement, you convey, or propagate by procuring conveyance of, a
-covered work, and grant a patent license to some of the parties
-receiving the covered work authorizing them to use, propagate, modify
-or convey a specific copy of the covered work, then the patent license
-you grant is automatically extended to all recipients of the covered
-work and works based on it.
-
-  A patent license is "discriminatory" if it does not include within
-the scope of its coverage, prohibits the exercise of, or is
-conditioned on the non-exercise of one or more of the rights that are
-specifically granted under this License.  You may not convey a covered
-work if you are a party to an arrangement with a third party that is
-in the business of distributing software, under which you make payment
-to the third party based on the extent of your activity of conveying
-the work, and under which the third party grants, to any of the
-parties who would receive the covered work from you, a discriminatory
-patent license (a) in connection with copies of the covered work
-conveyed by you (or copies made from those copies), or (b) primarily
-for and in connection with specific products or compilations that
-contain the covered work, unless you entered into that arrangement,
-or that patent license was granted, prior to 28 March 2007.
-
-  Nothing in this License shall be construed as excluding or limiting
-any implied license or other defenses to infringement that may
-otherwise be available to you under applicable patent law.
-
-  12. No Surrender of Others' Freedom.
-
-  If conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License.  If you cannot convey a
-covered work so as to satisfy simultaneously your obligations under this
-License and any other pertinent obligations, then as a consequence you may
-not convey it at all.  For example, if you agree to terms that obligate you
-to collect a royalty for further conveying from those to whom you convey
-the Program, the only way you could satisfy both those terms and this
-License would be to refrain entirely from conveying the Program.
-
-  13. Remote Network Interaction; Use with the GNU General Public License.
-
-  Notwithstanding any other provision of this License, if you modify the
-Program, your modified version must prominently offer all users
-interacting with it remotely through a computer network (if your version
-supports such interaction) an opportunity to receive the Corresponding
-Source of your version by providing access to the Corresponding Source
-from a network server at no charge, through some standard or customary
-means of facilitating copying of software.  This Corresponding Source
-shall include the Corresponding Source for any work covered by version 3
-of the GNU General Public License that is incorporated pursuant to the
-following paragraph.
-
-  Notwithstanding any other provision of this License, you have
-permission to link or combine any covered work with a work licensed
-under version 3 of the GNU General Public License into a single
-combined work, and to convey the resulting work.  The terms of this
-License will continue to apply to the part which is the covered work,
-but the work with which it is combined will remain governed by version
-3 of the GNU General Public License.
-
-  14. Revised Versions of this License.
-
-  The Free Software Foundation may publish revised and/or new versions of
-the GNU Affero General Public License from time to time.  Such new versions
-will be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-  Each version is given a distinguishing version number.  If the
-Program specifies that a certain numbered version of the GNU Affero General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation.  If the Program does not specify a version number of the
-GNU Affero General Public License, you may choose any version ever published
-by the Free Software Foundation.
-
-  If the Program specifies that a proxy can decide which future
-versions of the GNU Affero General Public License can be used, that proxy's
-public statement of acceptance of a version permanently authorizes you
-to choose that version for the Program.
-
-  Later license versions may give you additional or different
-permissions.  However, no additional obligations are imposed on any
-author or copyright holder as a result of your choosing to follow a
-later version.
-
-  15. Disclaimer of Warranty.
-
-  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
-OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
-  16. Limitation of Liability.
-
-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU Affero General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU Affero General Public License for more details.
-
-    You should have received a copy of the GNU Affero General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If your software can interact with users remotely through a computer
-network, you should also make sure that it provides a way for users to
-get its source.  For example, if your program is a web application, its
-interface could display a "Source" link that leads users to an archive
-of the code.  There are many ways you could offer source, and different
-solutions will be better for different programs; see section 13 for the
-specific requirements.
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU AGPL, see
-<https://www.gnu.org/licenses/>.

yolov5/README.md

@@ -1,513 +0,0 @@
-<div align="center">
-  <p>
-    <a href="https://www.ultralytics.com/blog/all-you-need-to-know-about-ultralytics-yolo11-and-its-applications" target="_blank">
-      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
-  </p>
-
-[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
-
-<div>
-    <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI Testing"></a>
-    <a href="https://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>
-    <a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
-    <br>
-    <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
-    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-    <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-  </div>
-  <br>
-
-Ultralytics YOLOv5 🚀 is a cutting-edge, state-of-the-art (SOTA) computer vision model developed by [Ultralytics](https://www.ultralytics.com/). Based on the [PyTorch](https://pytorch.org/) framework, YOLOv5 is renowned for its ease of use, speed, and accuracy. It incorporates insights and best practices from extensive research and development, making it a popular choice for a wide range of vision AI tasks, including [object detection](https://docs.ultralytics.com/tasks/detect/), [image segmentation](https://docs.ultralytics.com/tasks/segment/), and [image classification](https://docs.ultralytics.com/tasks/classify/).
-
-We hope the resources here help you get the most out of YOLOv5. Please browse the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for detailed information, raise an issue on [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) for support, and join our [Discord community](https://discord.com/invite/ultralytics) for questions and discussions!
-
-To request an Enterprise License, please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
-
-<div align="center">
-  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
-</div>
-
-</div>
-<br>
-
-## 🚀 YOLO11: The Next Evolution
-
-We are excited to announce the launch of **Ultralytics YOLO11** 🚀, the latest advancement in our state-of-the-art (SOTA) vision models! Available now at the [Ultralytics YOLO GitHub repository](https://github.com/ultralytics/ultralytics), YOLO11 builds on our legacy of speed, precision, and ease of use. Whether you're tackling [object detection](https://docs.ultralytics.com/tasks/detect/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [pose estimation](https://docs.ultralytics.com/tasks/pose/), [image classification](https://docs.ultralytics.com/tasks/classify/), or [oriented object detection (OBB)](https://docs.ultralytics.com/tasks/obb/), YOLO11 delivers the performance and versatility needed to excel in diverse applications.
-
-Get started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:
-
-[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://www.pepy.tech/projects/ultralytics)
-
-```bash
-# Install the ultralytics package
-pip install ultralytics
-```
-
-<div align="center">
-  <a href="https://www.ultralytics.com/yolo" target="_blank">
-  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="Ultralytics YOLO Performance Comparison"></a>
-</div>
-
-## 📚 Documentation
-
-See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing, and deployment. See below for quickstart examples.
-
-<details open>
-<summary>Install</summary>
-
-Clone the repository and install dependencies in a [**Python>=3.8.0**](https://www.python.org/) environment. Ensure you have [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) installed.
-
-```bash
-# Clone the YOLOv5 repository
-git clone https://github.com/ultralytics/yolov5
-
-# Navigate to the cloned directory
-cd yolov5
-
-# Install required packages
-pip install -r requirements.txt
-```
-
-</details>
-
-<details open>
-<summary>Inference with PyTorch Hub</summary>
-
-Use YOLOv5 via [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) for inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) are automatically downloaded from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
-
-```python
-import torch
-
-# Load a YOLOv5 model (options: yolov5n, yolov5s, yolov5m, yolov5l, yolov5x)
-model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # Default: yolov5s
-
-# Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list)
-img = "https://ultralytics.com/images/zidane.jpg"  # Example image
-
-# Perform inference (handles batching, resizing, normalization automatically)
-results = model(img)
-
-# Process the results (options: .print(), .show(), .save(), .crop(), .pandas())
-results.print()  # Print results to console
-results.show()  # Display results in a window
-results.save()  # Save results to runs/detect/exp
-```
-
-</details>
-
-<details>
-<summary>Inference with detect.py</summary>
-
-The `detect.py` script runs inference on various sources. It automatically downloads [models](https://github.com/ultralytics/yolov5/tree/master/models) from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saves the results to the `runs/detect` directory.
-
-```bash
-# Run inference using a webcam
-python detect.py --weights yolov5s.pt --source 0
-
-# Run inference on a local image file
-python detect.py --weights yolov5s.pt --source img.jpg
-
-# Run inference on a local video file
-python detect.py --weights yolov5s.pt --source vid.mp4
-
-# Run inference on a screen capture
-python detect.py --weights yolov5s.pt --source screen
-
-# Run inference on a directory of images
-python detect.py --weights yolov5s.pt --source path/to/images/
-
-# Run inference on a text file listing image paths
-python detect.py --weights yolov5s.pt --source list.txt
-
-# Run inference on a text file listing stream URLs
-python detect.py --weights yolov5s.pt --source list.streams
-
-# Run inference using a glob pattern for images
-python detect.py --weights yolov5s.pt --source 'path/to/*.jpg'
-
-# Run inference on a YouTube video URL
-python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4'
-
-# Run inference on an RTSP, RTMP, or HTTP stream
-python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4'
-```
-
-</details>
-
-<details>
-<summary>Training</summary>
-
-The commands below demonstrate how to reproduce YOLOv5 [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/) results. Both [models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) are downloaded automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are approximately 1/2/4/6/8 days on a single [NVIDIA V100 GPU](https://www.nvidia.com/en-us/data-center/v100/). Using [Multi-GPU training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) can significantly reduce training time. Use the largest `--batch-size` your hardware allows, or use `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). The batch sizes shown below are for V100-16GB GPUs.
-
-```bash
-# Train YOLOv5n on COCO for 300 epochs
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
-
-# Train YOLOv5s on COCO for 300 epochs
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64
-
-# Train YOLOv5m on COCO for 300 epochs
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40
-
-# Train YOLOv5l on COCO for 300 epochs
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24
-
-# Train YOLOv5x on COCO for 300 epochs
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16
-```
-
-<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" alt="YOLOv5 Training Results">
-
-</details>
-
-<details open>
-<summary>Tutorials</summary>
-
-- **[Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **RECOMMENDED**: Learn how to train YOLOv5 on your own datasets.
-- **[Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️: Improve your model's performance with expert tips.
-- **[Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**: Speed up training using multiple GPUs.
-- **[PyTorch Hub Integration](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **NEW**: Easily load models using PyTorch Hub.
-- **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀: Convert your models to various deployment formats like [ONNX](https://onnx.ai/) or [TensorRT](https://developer.nvidia.com/tensorrt).
-- **[NVIDIA Jetson Deployment](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/)** 🌟 **NEW**: Deploy YOLOv5 on [NVIDIA Jetson](https://developer.nvidia.com/embedded-computing) devices.
-- **[Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**: Enhance prediction accuracy with TTA.
-- **[Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**: Combine multiple models for better performance.
-- **[Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**: Optimize models for size and speed.
-- **[Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**: Automatically find the best training hyperparameters.
-- **[Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**: Adapt pretrained models to new tasks efficiently using [transfer learning](https://www.ultralytics.com/glossary/transfer-learning).
-- **[Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **NEW**: Understand the YOLOv5 model architecture.
-- **[Ultralytics HUB Training](https://www.ultralytics.com/hub)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics HUB.
-- **[ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**: Integrate with [ClearML](https://clear.ml/) for experiment tracking.
-- **[Neural Magic DeepSparse Integration](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**: Accelerate inference with DeepSparse.
-- **[Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **NEW**: Log experiments using [Comet ML](https://www.comet.com/).
-
-</details>
-
-## 🧩 Integrations
-
-Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/).
-
-<a href="https://docs.ultralytics.com/integrations/" target="_blank">
-    <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
-</a>
-<br>
-<br>
-
-<div align="center">
-  <a href="https://www.ultralytics.com/hub">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://docs.ultralytics.com/integrations/weights-biases/">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="Weights & Biases logo"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://docs.ultralytics.com/integrations/comet/">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://docs.ultralytics.com/integrations/neural-magic/">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
-</div>
-
-|                                                       Ultralytics HUB 🌟                                                       |                                                          Weights & Biases                                                           |                                                                              Comet                                                                              |                                                        Neural Magic                                                         |
-| :----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
-| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://hub.ultralytics.com). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |
-
-## ⭐ Ultralytics HUB
-
-Experience seamless AI development with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the ultimate platform for building, training, and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models. Visualize datasets, train [YOLOv5](https://docs.ultralytics.com/models/yolov5/) and [YOLOv8](https://docs.ultralytics.com/models/yolov8/) 🚀 models, and deploy them to real-world applications without writing any code. Transform images into actionable insights using our cutting-edge tools and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** today!
-
-<a align="center" href="https://www.ultralytics.com/hub" target="_blank">
-<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB Platform Screenshot"></a>
-
-## 🤔 Why YOLOv5?
-
-YOLOv5 is designed for simplicity and ease of use. We prioritize real-world performance and accessibility.
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png" alt="YOLOv5 Performance Chart"></p>
-<details>
-  <summary>YOLOv5-P5 640 Figure</summary>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png" alt="YOLOv5 P5 640 Performance Chart"></p>
-</details>
-<details>
-  <summary>Figure Notes</summary>
-
-- **COCO AP val** denotes the [mean Average Precision (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map) at [Intersection over Union (IoU)](https://www.ultralytics.com/glossary/intersection-over-union-iou) thresholds from 0.5 to 0.95, measured on the 5,000-image [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco/) across various inference sizes (256 to 1536 pixels).
-- **GPU Speed** measures the average inference time per image on the [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco/) using an [AWS p3.2xlarge V100 instance](https://aws.amazon.com/ec2/instance-types/p3/) with a batch size of 32.
-- **EfficientDet** data is sourced from the [google/automl repository](https://github.com/google/automl) at batch size 8.
-- **Reproduce** these results using the command: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-
-</details>
-
-### Pretrained Checkpoints
-
-This table shows the performance metrics for various YOLOv5 models trained on the COCO dataset.
-
-| Model                                                                                                                                                                    | Size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
-| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt)                                                                                       | 640                   | 28.0                 | 45.7              | **45**                       | **6.3**                       | **0.6**                        | **1.9**            | **4.5**                |
-| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt)                                                                                       | 640                   | 37.4                 | 56.8              | 98                           | 6.4                           | 0.9                            | 7.2                | 16.5                   |
-| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt)                                                                                       | 640                   | 45.4                 | 64.1              | 224                          | 8.2                           | 1.7                            | 21.2               | 49.0                   |
-| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt)                                                                                       | 640                   | 49.0                 | 67.3              | 430                          | 10.1                          | 2.7                            | 46.5               | 109.1                  |
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt)                                                                                       | 640                   | 50.7                 | 68.9              | 766                          | 12.1                          | 4.8                            | 86.7               | 205.7                  |
-|                                                                                                                                                                          |                       |                      |                   |                              |                               |                                |                    |                        |
-| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt)                                                                                     | 1280                  | 36.0                 | 54.4              | 153                          | 8.1                           | 2.1                            | 3.2                | 4.6                    |
-| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt)                                                                                     | 1280                  | 44.8                 | 63.7              | 385                          | 8.2                           | 3.6                            | 12.6               | 16.8                   |
-| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt)                                                                                     | 1280                  | 51.3                 | 69.3              | 887                          | 11.1                          | 6.8                            | 35.7               | 50.0                   |
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt)                                                                                     | 1280                  | 53.7                 | 71.3              | 1784                         | 15.8                          | 10.5                           | 76.8               | 111.4                  |
-| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) | 1280<br>1536          | 55.0<br>**55.8**     | 72.7<br>**72.7**  | 3136<br>-                    | 26.2<br>-                     | 19.4<br>-                      | 140.7<br>-         | 209.8<br>-             |
-
-<details>
-  <summary>Table Notes</summary>
-
-- All checkpoints were trained for 300 epochs using default settings. Nano (n) and Small (s) models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyperparameters, while Medium (m), Large (l), and Extra-Large (x) models use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
-- **mAP<sup>val</sup>** values represent single-model, single-scale performance on the [COCO val2017 dataset](https://docs.ultralytics.com/datasets/detect/coco/).<br>Reproduce using: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-- **Speed** metrics are averaged over COCO val images using an [AWS p3.2xlarge V100 instance](https://aws.amazon.com/ec2/instance-types/p3/). Non-Maximum Suppression (NMS) time (~1 ms/image) is not included.<br>Reproduce using: `python val.py --data coco.yaml --img 640 --task speed --batch 1`
-- **TTA** ([Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)) includes reflection and scale augmentations for improved accuracy.<br>Reproduce using: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
-
-</details>
-
-## 🖼️ Segmentation
-
-The YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) introduced [instance segmentation](https://docs.ultralytics.com/tasks/segment/) models that achieve state-of-the-art performance. These models are designed for easy training, validation, and deployment. For full details, see the [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and explore the [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart examples.
-
-<details>
-  <summary>Segmentation Checkpoints</summary>
-
-<div align="center">
-<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
-<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png" alt="YOLOv5 Segmentation Performance Chart"></a>
-</div>
-
-YOLOv5 segmentation models were trained on the [COCO dataset](https://docs.ultralytics.com/datasets/segment/coco/) for 300 epochs at an image size of 640 pixels using A100 GPUs. Models were exported to [ONNX](https://onnx.ai/) FP32 for CPU speed tests and [TensorRT](https://developer.nvidia.com/tensorrt) FP16 for GPU speed tests. All speed tests were conducted on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for reproducibility.
-
-| Model                                                                                      | Size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train Time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
-| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                   | 27.6                 | 23.4                  | 80:17                                         | **62.7**                       | **1.2**                        | **2.0**            | **7.1**                |
-| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                   | 37.6                 | 31.7                  | 88:16                                         | 173.3                          | 1.4                            | 7.6                | 26.4                   |
-| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                   | 45.0                 | 37.1                  | 108:36                                        | 427.0                          | 2.2                            | 22.0               | 70.8                   |
-| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640                   | 49.0                 | 39.9                  | 66:43 (2x)                                    | 857.4                          | 2.9                            | 47.9               | 147.7                  |
-| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640                   | **50.7**             | **41.4**              | 62:56 (3x)                                    | 1579.2                         | 4.5                            | 88.8               | 265.7                  |
-
-- All checkpoints were trained for 300 epochs using the SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at an image size of 640 pixels, using default settings.<br>Training runs are logged at [https://wandb.ai/glenn-jocher/YOLOv5_v70_official](https://wandb.ai/glenn-jocher/YOLOv5_v70_official).
-- **Accuracy** values represent single-model, single-scale performance on the COCO dataset.<br>Reproduce using: `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
-- **Speed** metrics are averaged over 100 inference images using a [Colab Pro A100 High-RAM instance](https://colab.research.google.com/signup). Values indicate inference speed only (NMS adds approximately 1ms per image).<br>Reproduce using: `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
-- **Export** to ONNX (FP32) and TensorRT (FP16) was performed using `export.py`.<br>Reproduce using: `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
-
-</details>
-
-<details>
-  <summary>Segmentation Usage Examples &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
-
-### Train
-
-YOLOv5 segmentation training supports automatic download of the [COCO128-seg dataset](https://docs.ultralytics.com/datasets/segment/coco8-seg/) via the `--data coco128-seg.yaml` argument. For the full [COCO-segments dataset](https://docs.ultralytics.com/datasets/segment/coco/), download it manually using `bash data/scripts/get_coco.sh --train --val --segments` and then train with `python train.py --data coco.yaml`.
-
-```bash
-# Train on a single GPU
-python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
-
-# Train using Multi-GPU Distributed Data Parallel (DDP)
-python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
-```
-
-### Val
-
-Validate the mask [mean Average Precision (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map) of YOLOv5s-seg on the COCO dataset:
-
-```bash
-# Download COCO validation segments split (780MB, 5000 images)
-bash data/scripts/get_coco.sh --val --segments
-
-# Validate the model
-python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640
-```
-
-### Predict
-
-Use the pretrained YOLOv5m-seg.pt model to perform segmentation on `bus.jpg`:
-
-```bash
-# Run prediction
-python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
-```
-
-```python
-# Load model from PyTorch Hub (Note: Inference support might vary)
-model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5m-seg.pt")
-```
-
-| ![Zidane Segmentation Example](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![Bus Segmentation Example](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
-| :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |
-
-### Export
-
-Export the YOLOv5s-seg model to ONNX and TensorRT formats:
-
-```bash
-# Export model
-python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
-```
-
-</details>
-
-## 🏷️ Classification
-
-YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases/v6.2) introduced support for [image classification](https://docs.ultralytics.com/tasks/classify/) model training, validation, and deployment. Check the [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) for details and the [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart guides.
-
-<details>
-  <summary>Classification Checkpoints</summary>
-
-<br>
-
-YOLOv5-cls classification models were trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) for 90 epochs using a 4xA100 instance. [ResNet](https://arxiv.org/abs/1512.03385) and [EfficientNet](https://arxiv.org/abs/1905.11946) models were trained alongside under identical settings for comparison. Models were exported to [ONNX](https://onnx.ai/) FP32 (CPU speed tests) and [TensorRT](https://developer.nvidia.com/tensorrt) FP16 (GPU speed tests). All speed tests were run on Google [Colab Pro](https://colab.research.google.com/signup) for reproducibility.
-
-| Model                                                                                              | Size<br><sup>(pixels) | Acc<br><sup>top1 | Acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
-| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
-| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                        | **0.5**                             | **2.5**            | **0.5**                |
-| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                            | 0.6                                 | 5.4                | 1.4                    |
-| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                           | 0.9                                 | 12.9               | 3.9                    |
-| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                           | 1.4                                 | 26.5               | 8.5                    |
-| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                           | 1.8                                 | 48.1               | 15.9                   |
-|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |
-| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                           | 0.5                                 | 11.7               | 3.7                    |
-| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt)               | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                           | 0.9                                 | 21.8               | 7.4                    |
-| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                           | 1.0                                 | 25.6               | 8.5                    |
-| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                           | 1.9                                 | 44.5               | 15.9                   |
-|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |
-| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                           | 1.3                                 | 5.3                | 1.0                    |
-| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                           | 1.6                                 | 7.8                | 1.5                    |
-| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                           | 1.6                                 | 9.1                | 1.7                    |
-| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                           | 1.9                                 | 12.2               | 2.4                    |
-
-<details>
-  <summary>Table Notes (click to expand)</summary>
-
-- All checkpoints were trained for 90 epochs using the SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at an image size of 224 pixels, using default settings.<br>Training runs are logged at [https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2](https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2).
-- **Accuracy** values (top-1 and top-5) represent single-model, single-scale performance on the [ImageNet-1k dataset](https://docs.ultralytics.com/datasets/classify/imagenet/).<br>Reproduce using: `python classify/val.py --data ../datasets/imagenet --img 224`
-- **Speed** metrics are averaged over 100 inference images using a Google [Colab Pro V100 High-RAM instance](https://colab.research.google.com/signup).<br>Reproduce using: `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
-- **Export** to ONNX (FP32) and TensorRT (FP16) was performed using `export.py`.<br>Reproduce using: `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
-
-</details>
-</details>
-
-<details>
-  <summary>Classification Usage Examples &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
-
-### Train
-
-YOLOv5 classification training supports automatic download for datasets like [MNIST](https://docs.ultralytics.com/datasets/classify/mnist/), [Fashion-MNIST](https://docs.ultralytics.com/datasets/classify/fashion-mnist/), [CIFAR10](https://docs.ultralytics.com/datasets/classify/cifar10/), [CIFAR100](https://docs.ultralytics.com/datasets/classify/cifar100/), [Imagenette](https://docs.ultralytics.com/datasets/classify/imagenette/), [Imagewoof](https://docs.ultralytics.com/datasets/classify/imagewoof/), and [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) using the `--data` argument. For example, start training on MNIST with `--data mnist`.
-
-```bash
-# Train on a single GPU using CIFAR-100 dataset
-python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
-
-# Train using Multi-GPU DDP on ImageNet dataset
-python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
-```
-
-### Val
-
-Validate the accuracy of the YOLOv5m-cls model on the ImageNet-1k validation dataset:
-
-```bash
-# Download ImageNet validation split (6.3GB, 50,000 images)
-bash data/scripts/get_imagenet.sh --val
-
-# Validate the model
-python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224
-```
-
-### Predict
-
-Use the pretrained YOLOv5s-cls.pt model to classify the image `bus.jpg`:
-
-```bash
-# Run prediction
-python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
-```
-
-```python
-# Load model from PyTorch Hub
-model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")
-```
-
-### Export
-
-Export trained YOLOv5s-cls, ResNet50, and EfficientNet_b0 models to ONNX and TensorRT formats:
-
-```bash
-# Export models
-python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
-```
-
-</details>
-
-## ☁️ Environments
-
-Get started quickly with our pre-configured environments. Click the icons below for setup details.
-
-<div align="center">
-  <a href="https://bit.ly/yolov5-paperspace-notebook" title="Run on Paperspace Gradient">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" title="Open in Google Colab">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://www.kaggle.com/models/ultralytics/yolov5" title="Open in Kaggle">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://hub.docker.com/r/ultralytics/yolov5" title="Pull Docker Image">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/" title="AWS Quickstart Guide">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/" title="GCP Quickstart Guide">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
-</div>
-
-## 🤝 Contribute
-
-We welcome your contributions! Making YOLOv5 accessible and effective is a community effort. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Share your feedback through the [YOLOv5 Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). Thank you to all our contributors for making YOLOv5 better!
-
-[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors)
-
-## 📜 License
-
-Ultralytics provides two licensing options to meet different needs:
-
-- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-v3) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.
-- **Enterprise License**: Tailored for commercial applications, this license allows seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
-
-## 📧 Contact
-
-For bug reports and feature requests related to YOLOv5, please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For general questions, discussions, and community support, join our [Discord server](https://discord.com/invite/ultralytics)!
-
-<br>
-<div align="center">
-  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
-</div>

yolov5/README.zh-CN.md

@@ -1,513 +0,0 @@
-<div align="center">
-  <p>
-    <a href="https://www.ultralytics.com/blog/all-you-need-to-know-about-ultralytics-yolo11-and-its-applications" target="_blank">
-      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO 横幅"></a>
-  </p>
-
-[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
-
-<div>
-    <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI 测试"></a>
-    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 引用"></a>
-    <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker 拉取次数"></a>
-    <a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics 论坛" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
-    <br>
-    <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="在 Gradient 上运行"></a>
-    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>
-    <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="在 Kaggle 中打开"></a>
-  </div>
-  <br>
-
-Ultralytics YOLOv5 🚀 是由 [Ultralytics](https://www.ultralytics.com/) 开发的尖端、达到业界顶尖水平（SOTA）的计算机视觉模型。基于 [PyTorch](https://pytorch.org/) 框架，YOLOv5 以其易用性、速度和准确性而闻名。它融合了广泛研究和开发的见解与最佳实践，使其成为各种视觉 AI 任务的热门选择，包括[目标检测](https://docs.ultralytics.com/tasks/detect/)、[图像分割](https://docs.ultralytics.com/tasks/segment/)和[图像分类](https://docs.ultralytics.com/tasks/classify/)。
-
-我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 [YOLOv5 文档](https://docs.ultralytics.com/yolov5/)获取详细信息，在 [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) 上提出 issue 以获得支持，并加入我们的 [Discord 社区](https://discord.com/invite/ultralytics)进行提问和讨论！
-
-如需申请企业许可证，请填写 [Ultralytics 授权许可](https://www.ultralytics.com/license) 表格。
-
-<div align="center">
-  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="2%" alt="Ultralytics BiliBili"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
-  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
-</div>
-
-</div>
-<br>
-
-## 🚀 YOLO11：下一代进化
-
-我们激动地宣布推出 **Ultralytics YOLO11** 🚀，这是我们业界顶尖（SOTA）视觉模型的最新进展！YOLO11 现已在 [Ultralytics YOLO GitHub 仓库](https://github.com/ultralytics/ultralytics)发布，它继承了我们速度快、精度高和易于使用的传统。无论您是处理[目标检测](https://docs.ultralytics.com/tasks/detect/)、[实例分割](https://docs.ultralytics.com/tasks/segment/)、[姿态估计](https://docs.ultralytics.com/tasks/pose/)、[图像分类](https://docs.ultralytics.com/tasks/classify/)还是[旋转目标检测 (OBB)](https://docs.ultralytics.com/tasks/obb/)，YOLO11 都能提供在多样化应用中脱颖而出所需的性能和多功能性。
-
-立即开始，释放 YOLO11 的全部潜力！访问 [Ultralytics 文档](https://docs.ultralytics.com/)获取全面的指南和资源：
-
-[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://www.pepy.tech/projects/ultralytics)
-
-```bash
-# 安装 ultralytics 包
-pip install ultralytics
-```
-
-<div align="center">
-  <a href="https://www.ultralytics.com/yolo" target="_blank">
-  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png" alt="Ultralytics YOLO 性能比较"></a>
-</div>
-
-## 📚 文档
-
-请参阅 [YOLOv5 文档](https://docs.ultralytics.com/yolov5/)，了解有关训练、测试和部署的完整文档。请参阅下方的快速入门示例。
-
-<details open>
-<summary>安装</summary>
-
-克隆仓库并在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装依赖项。确保您已安装 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。
-
-```bash
-# 克隆 YOLOv5 仓库
-git clone https://github.com/ultralytics/yolov5
-
-# 导航到克隆的目录
-cd yolov5
-
-# 安装所需的包
-pip install -r requirements.txt
-```
-
-</details>
-
-<details open>
-<summary>使用 PyTorch Hub 进行推理</summary>
-
-通过 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 使用 YOLOv5 进行推理。[模型](https://github.com/ultralytics/yolov5/tree/master/models) 会自动从最新的 YOLOv5 [发布版本](https://github.com/ultralytics/yolov5/releases)下载。
-
-```python
-import torch
-
-# 加载 YOLOv5 模型（选项：yolov5n, yolov5s, yolov5m, yolov5l, yolov5x）
-model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # 默认：yolov5s
-
-# 定义输入图像源（URL、本地文件、PIL 图像、OpenCV 帧、numpy 数组或列表）
-img = "https://ultralytics.com/images/zidane.jpg"  # 示例图像
-
-# 执行推理（自动处理批处理、调整大小、归一化）
-results = model(img)
-
-# 处理结果（选项：.print(), .show(), .save(), .crop(), .pandas()）
-results.print()  # 将结果打印到控制台
-results.show()  # 在窗口中显示结果
-results.save()  # 将结果保存到 runs/detect/exp
-```
-
-</details>
-
-<details>
-<summary>使用 detect.py 进行推理</summary>
-
-`detect.py` 脚本在各种来源上运行推理。它会自动从最新的 YOLOv5 [发布版本](https://github.com/ultralytics/yolov5/releases)下载[模型](https://github.com/ultralytics/yolov5/tree/master/models)，并将结果保存到 `runs/detect` 目录。
-
-```bash
-# 使用网络摄像头运行推理
-python detect.py --weights yolov5s.pt --source 0
-
-# 对本地图像文件运行推理
-python detect.py --weights yolov5s.pt --source img.jpg
-
-# 对本地视频文件运行推理
-python detect.py --weights yolov5s.pt --source vid.mp4
-
-# 对屏幕截图运行推理
-python detect.py --weights yolov5s.pt --source screen
-
-# 对图像目录运行推理
-python detect.py --weights yolov5s.pt --source path/to/images/
-
-# 对列出图像路径的文本文件运行推理
-python detect.py --weights yolov5s.pt --source list.txt
-
-# 对列出流 URL 的文本文件运行推理
-python detect.py --weights yolov5s.pt --source list.streams
-
-# 使用 glob 模式对图像运行推理
-python detect.py --weights yolov5s.pt --source 'path/to/*.jpg'
-
-# 对 YouTube 视频 URL 运行推理
-python detect.py --weights yolov5s.pt --source 'https://youtu.be/LNwODJXcvt4'
-
-# 对 RTSP、RTMP 或 HTTP 流运行推理
-python detect.py --weights yolov5s.pt --source 'rtsp://example.com/media.mp4'
-```
-
-</details>
-
-<details>
-<summary>训练</summary>
-
-以下命令演示了如何复现 YOLOv5 在 [COCO 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上的结果。[模型](https://github.com/ultralytics/yolov5/tree/master/models)和[数据集](https://github.com/ultralytics/yolov5/tree/master/data)都会自动从最新的 YOLOv5 [发布版本](https://github.com/ultralytics/yolov5/releases)下载。YOLOv5n/s/m/l/x 的训练时间在单个 [NVIDIA V100 GPU](https://www.nvidia.com/en-us/data-center/v100/) 上大约需要 1/2/4/6/8 天。使用[多 GPU 训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)可以显著减少训练时间。请使用硬件允许的最大 `--batch-size`，或使用 `--batch-size -1` 以启用 YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092)。下面显示的批处理大小适用于 V100-16GB GPU。
-
-```bash
-# 在 COCO 上训练 YOLOv5n 300 个周期
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
-
-# 在 COCO 上训练 YOLOv5s 300 个周期
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5s.yaml --batch-size 64
-
-# 在 COCO 上训练 YOLOv5m 300 个周期
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5m.yaml --batch-size 40
-
-# 在 COCO 上训练 YOLOv5l 300 个周期
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5l.yaml --batch-size 24
-
-# 在 COCO 上训练 YOLOv5x 300 个周期
-python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5x.yaml --batch-size 16
-```
-
-<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" alt="YOLOv5 训练结果">
-
-</details>
-
-<details open>
-<summary>教程</summary>
-
-- **[训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **推荐**：学习如何在您自己的数据集上训练 YOLOv5。
-- **[获得最佳训练结果的技巧](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️：利用专家技巧提升模型性能。
-- **[多 GPU 训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**：使用多个 GPU 加速训练。
-- **[PyTorch Hub 集成](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **新增**：使用 PyTorch Hub 轻松加载模型。
-- **[模型导出 (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀：将您的模型转换为各种部署格式，如 [ONNX](https://onnx.ai/) 或 [TensorRT](https://developer.nvidia.com/tensorrt)。
-- **[NVIDIA Jetson 部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/)** 🌟 **新增**：在 [NVIDIA Jetson](https://developer.nvidia.com/embedded-computing) 设备上部署 YOLOv5。
-- **[测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**：使用 TTA 提高预测准确性。
-- **[模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**：组合多个模型以获得更好的性能。
-- **[模型剪枝/稀疏化](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**：优化模型的大小和速度。
-- **[超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**：自动找到最佳训练超参数。
-- **[使用冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**：使用[迁移学习](https://www.ultralytics.com/glossary/transfer-learning)高效地将预训练模型应用于新任务。
-- **[架构摘要](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **新增**：了解 YOLOv5 模型架构。
-- **[Ultralytics HUB 训练](https://www.ultralytics.com/hub)** 🚀 **推荐**：使用 Ultralytics HUB 训练和部署 YOLO 模型。
-- **[ClearML 日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**：与 [ClearML](https://clear.ml/) 集成以进行实验跟踪。
-- **[Neural Magic DeepSparse 集成](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**：使用 DeepSparse 加速推理。
-- **[Comet 日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **新增**：使用 [Comet ML](https://www.comet.com/) 记录实验。
-
-</details>
-
-## 🧩 集成
-
-我们与领先 AI 平台的关键集成扩展了 Ultralytics 产品的功能，增强了诸如数据集标注、训练、可视化和模型管理等任务。了解 Ultralytics 如何与 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/)、[Comet ML](https://docs.ultralytics.com/integrations/comet/)、[Roboflow](https://docs.ultralytics.com/integrations/roboflow/) 和 [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 等合作伙伴协作，优化您的 AI 工作流程。在 [Ultralytics 集成](https://docs.ultralytics.com/integrations/) 探索更多信息。
-
-<a href="https://docs.ultralytics.com/integrations/" target="_blank">
-    <img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics 主动学习集成">
-</a>
-<br>
-<br>
-
-<div align="center">
-  <a href="https://www.ultralytics.com/hub">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png" width="10%" alt="Ultralytics HUB logo"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://docs.ultralytics.com/integrations/weights-biases/">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png" width="10%" alt="Weights & Biases logo"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://docs.ultralytics.com/integrations/comet/">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
-  <a href="https://docs.ultralytics.com/integrations/neural-magic/">
-    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
-</div>
-
-|                                            Ultralytics HUB 🌟                                            |                                              Weights & Biases                                               |                                                           Comet                                                            |                                                      Neural Magic                                                       |
-| :------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------: |
-| 简化 YOLO 工作流程：使用 [Ultralytics HUB](https://hub.ultralytics.com) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/) 跟踪实验、超参数和结果。 | 永久免费的 [Comet ML](https://docs.ultralytics.com/integrations/comet/) 让您保存 YOLO 模型、恢复训练并交互式地可视化预测。 | 使用 [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/) 将 YOLO 推理速度提高多达 6 倍。 |
-
-## ⭐ Ultralytics HUB
-
-通过 [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐ 体验无缝的 AI 开发，这是构建、训练和部署[计算机视觉](https://www.ultralytics.com/glossary/computer-vision-cv)模型的终极平台。可视化数据集，训练 [YOLOv5](https://docs.ultralytics.com/models/yolov5/) 和 [YOLOv8](https://docs.ultralytics.com/models/yolov8/) 🚀 模型，并将它们部署到实际应用中，无需编写任何代码。使用我们尖端的工具和用户友好的 [Ultralytics App](https://www.ultralytics.com/app-install) 将图像转化为可操作的见解。今天就**免费**开始您的旅程吧！
-
-<a align="center" href="https://www.ultralytics.com/hub" target="_blank">
-<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB 平台截图"></a>
-
-## 🤔 为何选择 YOLOv5？
-
-YOLOv5 的设计旨在简单易用。我们优先考虑实际性能和可访问性。
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png" alt="YOLOv5 性能图表"></p>
-<details>
-  <summary>YOLOv5-P5 640 图表</summary>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png" alt="YOLOv5 P5 640 性能图表"></p>
-</details>
-<details>
-  <summary>图表说明</summary>
-
-- **COCO AP val** 表示在 [交并比 (IoU)](https://www.ultralytics.com/glossary/intersection-over-union-iou) 阈值从 0.5 到 0.95 范围内的[平均精度均值 (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map)，在包含 5000 张图像的 [COCO val2017 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上，使用各种推理尺寸（256 到 1536 像素）测量得出。
-- **GPU Speed** 使用批处理大小为 32 的 [AWS p3.2xlarge V100 实例](https://aws.amazon.com/ec2/instance-types/p3/)，测量在 [COCO val2017 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上每张图像的平均推理时间���
-- **EfficientDet** 数据来源于 [google/automl 仓库](https://github.com/google/automl)，批处理大小为 8。
-- **复现**这些结果请使用命令：`python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-
-</details>
-
-### 预训练权重
-
-此表显示了在 COCO 数据集上训练的各种 YOLOv5 模型的性能指标。
-
-| 模型                                                                                                                                                                     | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 速度<br><sup>CPU b1<br>(毫秒) | 速度<br><sup>V100 b1<br>(毫秒) | 速度<br><sup>V100 b32<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------- | ----------------------------- | ------------------------------ | ------------------------------- | ---------------- | ---------------------- |
-| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt)                                                                                       | 640                 | 28.0                 | 45.7              | **45**                        | **6.3**                        | **0.6**                         | **1.9**          | **4.5**                |
-| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt)                                                                                       | 640                 | 37.4                 | 56.8              | 98                            | 6.4                            | 0.9                             | 7.2              | 16.5                   |
-| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt)                                                                                       | 640                 | 45.4                 | 64.1              | 224                           | 8.2                            | 1.7                             | 21.2             | 49.0                   |
-| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt)                                                                                       | 640                 | 49.0                 | 67.3              | 430                           | 10.1                           | 2.7                             | 46.5             | 109.1                  |
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt)                                                                                       | 640                 | 50.7                 | 68.9              | 766                           | 12.1                           | 4.8                             | 86.7             | 205.7                  |
-|                                                                                                                                                                          |                     |                      |                   |                               |                                |                                 |                  |                        |
-| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt)                                                                                     | 1280                | 36.0                 | 54.4              | 153                           | 8.1                            | 2.1                             | 3.2              | 4.6                    |
-| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt)                                                                                     | 1280                | 44.8                 | 63.7              | 385                           | 8.2                            | 3.6                             | 12.6             | 16.8                   |
-| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt)                                                                                     | 1280                | 51.3                 | 69.3              | 887                           | 11.1                           | 6.8                             | 35.7             | 50.0                   |
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt)                                                                                     | 1280                | 53.7                 | 71.3              | 1784                          | 15.8                           | 10.5                            | 76.8             | 111.4                  |
-| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [[TTA]](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) | 1280<br>1536        | 55.0<br>**55.8**     | 72.7<br>**72.7**  | 3136<br>-                     | 26.2<br>-                      | 19.4<br>-                       | 140.7<br>-       | 209.8<br>-             |
-
-<details>
-  <summary>表格说明</summary>
-
-- 所有预训练权重均使用默认设置训练了 300 个周期。Nano (n) 和 Small (s) 模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) 超参数，而 Medium (m)、Large (l) 和 Extra-Large (x) 模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml)。
-- **mAP<sup>val</sup>** 值表示在 [COCO val2017 数据集](https://docs.ultralytics.com/datasets/detect/coco/)上的单模型、单尺度性能。<br>复现请使用：`python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-- **速度**指标是在 [AWS p3.2xlarge V100 实例](https://aws.amazon.com/ec2/instance-types/p3/)上对 COCO val 图像进行平均测量的。不包括非极大值抑制 (NMS) 时间（约 1 毫秒/图像）。<br>复现请使用：`python val.py --data coco.yaml --img 640 --task speed --batch 1`
-- **TTA** ([测试时增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)) 包括反射和尺度增强以提高准确性。<br>复现请使用：`python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
-
-</details>
-
-## 🖼️ 分割
-
-YOLOv5 [v7.0 版本](https://github.com/ultralytics/yolov5/releases/v7.0) 引入了[实例分割](https://docs.ultralytics.com/tasks/segment/)模型，达到了业界顶尖的性能。这些模型设计用于轻松训练、验证和部署。有关完整详细信息，请参阅[发布说明](https://github.com/ultralytics/yolov5/releases/v7.0)，并探索 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb)以获取快速入门示例。
-
-<details>
-  <summary>分割预训练权重</summary>
-
-<div align="center">
-<a align="center" href="https://www.ultralytics.com/yolo" target="_blank">
-<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png" alt="YOLOv5 分割性能图表"></a>
-</div>
-
-YOLOv5 分割模型在 [COCO 数据集](https://docs.ultralytics.com/datasets/segment/coco/)上使用 A100 GPU 以 640 像素的图像大小训练了 300 个周期。模型导出为 [ONNX](https://onnx.ai/) FP32 用于 CPU 速度测试，导出为 [TensorRT](https://developer.nvidia.com/tensorrt) FP16 用于 GPU 速度测试。所有速度测试均在 Google [Colab Pro](https://colab.research.google.com/signup) 笔记本上进行，以确保可复现性。
-
-| 模型                                                                                       | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时间<br><sup>300 周期<br>A100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TRT A100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| ------------------------------------------------------------------------------------------ | ------------------- | -------------------- | --------------------- | ---------------------------------------- | ------------------------------- | ------------------------------- | ---------------- | ---------------------- |
-| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                 | 27.6                 | 23.4                  | 80:17                                    | **62.7**                        | **1.2**                         | **2.0**          | **7.1**                |
-| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                 | 37.6                 | 31.7                  | 88:16                                    | 173.3                           | 1.4                             | 7.6              | 26.4                   |
-| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                 | 45.0                 | 37.1                  | 108:36                                   | 427.0                           | 2.2                             | 22.0             | 70.8                   |
-| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640                 | 49.0                 | 39.9                  | 66:43 (2x)                               | 857.4                           | 2.9                             | 47.9             | 147.7                  |
-| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640                 | **50.7**             | **41.4**              | 62:56 (3x)                               | 1579.2                          | 4.5                             | 88.8             | 265.7                  |
-
-- 所有预训练权重均使用 SGD 优化器，`lr0=0.01` 和 `weight_decay=5e-5`，在 640 像素的图像大小下，使用默认设置训练了 300 个周期。<br>训练运行记录在 [https://wandb.ai/glenn-jocher/YOLOv5_v70_official](https://wandb.ai/glenn-jocher/YOLOv5_v70_official)。
-- **准确度**值表示在 COCO 数据集上的单模型、单尺度性能。<br>复现请使用：`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
-- **速度**指标是在 [Colab Pro A100 High-RAM 实例](https://colab.research.google.com/signup)上对 100 张推理图像进行平均测量的。值仅表示推理速度（NMS 约增加 1 毫秒/图像）。<br>复现请使用：`python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
-- **导出**到 ONNX (FP32) 和 TensorRT (FP16) 是使用 `export.py` 完成的。<br>复现请使用：`python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
-
-</details>
-
-<details>
-  <summary>分割使用示例 &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a></summary>
-
-### 训练
-
-YOLOv5 分割训练支持通过 `--data coco128-seg.yaml` 参数自动下载 [COCO128-seg 数据集](https://docs.ultralytics.com/datasets/segment/coco8-seg/)。对于完整的 [COCO-segments 数据集](https://docs.ultralytics.com/datasets/segment/coco/)，请使用 `bash data/scripts/get_coco.sh --train --val --segments` 手动下载，然后使用 `python train.py --data coco.yaml` 进行训练。
-
-```bash
-# 在单个 GPU 上训练
-python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
-
-# 使用多 GPU 分布式数据并行 (DDP) 进行训练
-python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
-```
-
-### 验证
-
-在 COCO 数据集上验证 YOLOv5s-seg 的掩码[平均精度均值 (mAP)](https://www.ultralytics.com/glossary/mean-average-precision-map)：
-
-```bash
-# 下载 COCO 验证分割集 (780MB, 5000 张图像)
-bash data/scripts/get_coco.sh --val --segments
-
-# 验证模型
-python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640
-```
-
-### 预测
-
-使用预训练的 YOLOv5m-seg.pt 模型对 `bus.jpg` 执行分割：
-
-```bash
-# 运行预测
-python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
-```
-
-```python
-# 从 PyTorch Hub 加载模型（注意：推理支持可能有所不同）
-model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5m-seg.pt")
-```
-
-| ![Zidane 分割示例](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![Bus 分割示例](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
-| :-----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: |
-
-### 导出
-
-将 YOLOv5s-seg 模型导出为 ONNX 和 TensorRT 格式：
-
-```bash
-# 导出模型
-python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
-```
-
-</details>
-
-## 🏷️ 分类
-
-YOLOv5 [v6.2 版本](https://github.com/ultralytics/yolov5/releases/v6.2) 引入了对[图像分类](https://docs.ultralytics.com/tasks/classify/)模型训练、验证和部署的支持。请查看[发布说明](https://github.com/ultralytics/yolov5/releases/v6.2)了解详细信息，并参阅 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb)获取快速入门指南。
-
-<details>
-  <summary>分类预训练权重</summary>
-
-<br>
-
-YOLOv5-cls 分类模型在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 上使用 4xA100 实例训练了 90 个周期。[ResNet](https://arxiv.org/abs/1512.03385) 和 [EfficientNet](https://arxiv.org/abs/1905.11946) 模型在相同设置下一起训练以进行比较。模型导出为 [ONNX](https://onnx.ai/) FP32（用于 CPU 速度测试）和 [TensorRT](https://developer.nvidia.com/tensorrt) FP16（用于 GPU 速度测试）。所有速度测试均在 Google [Colab Pro](https://colab.research.google.com/signup) 上运行，以确保可复现性。
-
-| 模型                                                                                               | 尺寸<br><sup>(像素) | 准确率<br><sup>top1 | 准确率<br><sup>top5 | 训练<br><sup>90 周期<br>4xA100 (小时) | 速度<br><sup>ONNX CPU<br>(毫秒) | 速度<br><sup>TensorRT V100<br>(毫秒) | 参数<br><sup>(M) | FLOPs<br><sup>@224 (B) |
-| -------------------------------------------------------------------------------------------------- | ------------------- | ------------------- | ------------------- | ------------------------------------- | ------------------------------- | ------------------------------------ | ---------------- | ---------------------- |
-| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt)         | 224                 | 64.6                | 85.4                | 7:59                                  | **3.3**                         | **0.5**                              | **2.5**          | **0.5**                |
-| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt)         | 224                 | 71.5                | 90.2                | 8:09                                  | 6.6                             | 0.6                                  | 5.4              | 1.4                    |
-| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt)         | 224                 | 75.9                | 92.9                | 10:06                                 | 15.5                            | 0.9                                  | 12.9             | 3.9                    |
-| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt)         | 224                 | 78.0                | 94.0                | 11:56                                 | 26.9                            | 1.4                                  | 26.5             | 8.5                    |
-| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt)         | 224                 | **79.0**            | **94.4**            | 15:04                                 | 54.3                            | 1.8                                  | 48.1             | 15.9                   |
-|                                                                                                    |                     |                     |                     |                                       |                                 |                                      |                  |                        |
-| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt)               | 224                 | 70.3                | 89.5                | **6:47**                              | 11.2                            | 0.5                                  | 11.7             | 3.7                    |
-| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt)               | 224                 | 73.9                | 91.8                | 8:33                                  | 20.6                            | 0.9                                  | 21.8             | 7.4                    |
-| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt)               | 224                 | 76.8                | 93.4                | 11:10                                 | 23.4                            | 1.0                                  | 25.6             | 8.5                    |
-| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt)             | 224                 | 78.5                | 94.3                | 17:10                                 | 42.1                            | 1.9                                  | 44.5             | 15.9                   |
-|                                                                                                    |                     |                     |                     |                                       |                                 |                                      |                  |                        |
-| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224                 | 75.1                | 92.4                | 13:03                                 | 12.5                            | 1.3                                  | 5.3              | 1.0                    |
-| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224                 | 76.4                | 93.2                | 17:04                                 | 14.9                            | 1.6                                  | 7.8              | 1.5                    |
-| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224                 | 76.6                | 93.4                | 17:10                                 | 15.9                            | 1.6                                  | 9.1              | 1.7                    |
-| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224                 | 77.7                | 94.0                | 19:19                                 | 18.9                            | 1.9                                  | 12.2             | 2.4                    |
-
-<details>
-  <summary>表格说明（点击展开）</summary>
-
-- 所有预训练权重均使用 SGD 优化器，`lr0=0.001` 和 `weight_decay=5e-5`，在 224 像素的图像大小下，使用默认设置训练了 90 个周期。<br>训练运行记录在 [https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2](https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2)。
-- **准确度**值（top-1 和 top-5）表示在 [ImageNet-1k 数据集](https://docs.ultralytics.com/datasets/classify/imagenet/)上的单模型、单尺度性能。<br>复现请使用：`python classify/val.py --data ../datasets/imagenet --img 224`
-- **速度**指标是在 Google [Colab Pro V100 High-RAM 实例](https://colab.research.google.com/signup)上对 100 张推理图像进行平均测量的。<br>复现请使用：`python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
-- **导出**到 ONNX (FP32) 和 TensorRT (FP16) 是使用 `export.py` 完成的。<br>复现请使用：`python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
-
-</details>
-</details>
-
-<details>
-  <summary>分类使用示例 &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a></summary>
-
-### 训练
-
-YOLOv5 分类训练支持使用 `--data` 参数自动下载诸如 [MNIST](https://docs.ultralytics.com/datasets/classify/mnist/)、[Fashion-MNIST](https://docs.ultralytics.com/datasets/classify/fashion-mnist/)、[CIFAR10](https://docs.ultralytics.com/datasets/classify/cifar10/)、[CIFAR100](https://docs.ultralytics.com/datasets/classify/cifar100/)、[Imagenette](https://docs.ultralytics.com/datasets/classify/imagenette/)、[Imagewoof](https://docs.ultralytics.com/datasets/classify/imagewoof/) 和 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 等数据集。例如，使用 `--data mnist` 开始在 MNIST 上训练。
-
-```bash
-# 使用 CIFAR-100 数据集在单个 GPU 上训练
-python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
-
-# 在 ImageNet 数据集上使用多 GPU DDP 进行训练
-python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
-```
-
-### 验证
-
-在 ImageNet-1k 验证数据集上验证 YOLOv5m-cls 模型的准确性：
-
-```bash
-# 下载 ImageNet 验证集 (6.3GB, 50,000 张图像)
-bash data/scripts/get_imagenet.sh --val
-
-# 验证模型
-python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224
-```
-
-### 预测
-
-使用预训练的 YOLOv5s-cls.pt 模型对图像 `bus.jpg` 进行分类：
-
-```bash
-# 运行预测
-python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
-```
-
-```python
-# 从 PyTorch Hub 加载模型
-model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")
-```
-
-### 导出
-
-将训练好的 YOLOv5s-cls、ResNet50 和 EfficientNet_b0 模型导出为 ONNX 和 TensorRT 格式：
-
-```bash
-# 导出模型
-python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
-```
-
-</details>
-
-## ☁️ 环境
-
-使用我们预配置的环境快速开始。点击下面的图标查看设置详情。
-
-<div align="center">
-  <a href="https://bit.ly/yolov5-paperspace-notebook" title="在 Paperspace Gradient 上运行">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" title="在 Google Colab 中打开">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://www.kaggle.com/models/ultralytics/yolov5" title="在 Kaggle 中打开">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://hub.docker.com/r/ultralytics/yolov5" title="拉取 Docker 镜像">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/" title="AWS 快速入门指南">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
-  <a href="https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/" title="GCP 快速入门指南">
-    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
-</div>
-
-## 🤝 贡献
-
-我们欢迎您的贡献！让 YOLOv5 变得易于访问和有效是社区的共同努力。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing/)开始。通过 [YOLOv5 调查](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)分享您的反馈。感谢所有为使 YOLOv5 变得更好而做出贡献的人！
-
-[![Ultralytics 开源贡献者](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov5/graphs/contributors)
-
-## 📜 许可证
-
-Ultralytics 提供两种许可选项以满足不同需求：
-
-- **AGPL-3.0 许可证**：一种 [OSI 批准的](https://opensource.org/license/agpl-v3)开源许可证，非常适合学术研究、个人项目和测试。它促进开放协作和知识共享。详情请参阅 [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件。
-- **企业许可证**：专为商业应用量身定制，此许可证允许将 Ultralytics 软件和 AI 模型无缝集成到商业产品和服务中，绕过 AGPL-3.0 的开源要求。对于商业用例，请通过 [Ultralytics 授权许可](https://www.ultralytics.com/license)联系我们。
-
-## 📧 联系
-
-对于与 YOLOv5 相关的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。对于一般问题、讨论和社区支持，请加入我们的 [Discord 服务器](https://discord.com/invite/ultralytics)！
-
-<br>
-<div align="center">
-  <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://youtube.com/ultralytics?sub_confirmation=1"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
-</div>

yolov5/benchmarks.py

@@ -1,294 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-"""
-Run YOLOv5 benchmarks on all supported export formats.
-
-Format                      | `export.py --include`         | Model
----                         | ---                           | ---
-PyTorch                     | -                             | yolov5s.pt
-TorchScript                 | `torchscript`                 | yolov5s.torchscript
-ONNX                        | `onnx`                        | yolov5s.onnx
-OpenVINO                    | `openvino`                    | yolov5s_openvino_model/
-TensorRT                    | `engine`                      | yolov5s.engine
-CoreML                      | `coreml`                      | yolov5s.mlpackage
-TensorFlow SavedModel       | `saved_model`                 | yolov5s_saved_model/
-TensorFlow GraphDef         | `pb`                          | yolov5s.pb
-TensorFlow Lite             | `tflite`                      | yolov5s.tflite
-TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
-TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
-
-Requirements:
-    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
-    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
-    $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # TensorRT
-
-Usage:
-    $ python benchmarks.py --weights yolov5s.pt --img 640
-"""
-
-import argparse
-import platform
-import sys
-import time
-from pathlib import Path
-
-import pandas as pd
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-# ROOT = ROOT.relative_to(Path.cwd())  # relative
-
-import export
-from models.experimental import attempt_load
-from models.yolo import SegmentationModel
-from segment.val import run as val_seg
-from utils import notebook_init
-from utils.general import LOGGER, check_yaml, file_size, print_args
-from utils.torch_utils import select_device
-from val import run as val_det
-
-
-def run(
-    weights=ROOT / "yolov5s.pt",  # weights path
-    imgsz=640,  # inference size (pixels)
-    batch_size=1,  # batch size
-    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
-    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    half=False,  # use FP16 half-precision inference
-    test=False,  # test exports only
-    pt_only=False,  # test PyTorch only
-    hard_fail=False,  # throw error on benchmark failure
-):
-    """
-    Run YOLOv5 benchmarks on multiple export formats and log results for model performance evaluation.
-
-    Args:
-        weights (Path | str): Path to the model weights file (default: ROOT / "yolov5s.pt").
-        imgsz (int): Inference size in pixels (default: 640).
-        batch_size (int): Batch size for inference (default: 1).
-        data (Path | str): Path to the dataset.yaml file (default: ROOT / "data/coco128.yaml").
-        device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu' (default: "").
-        half (bool): Use FP16 half-precision inference (default: False).
-        test (bool): Test export formats only (default: False).
-        pt_only (bool): Test PyTorch format only (default: False).
-        hard_fail (bool): Throw an error on benchmark failure if True (default: False).
-
-    Returns:
-        None. Logs information about the benchmark results, including the format, size, mAP50-95, and inference time.
-
-    Notes:
-        Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML,
-            TensorFlow SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js
-            are unsupported.
-
-    Example:
-        ```python
-        $ python benchmarks.py --weights yolov5s.pt --img 640
-        ```
-
-    Usage:
-        Install required packages:
-          $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU support
-          $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow   # GPU support
-          $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # TensorRT
-
-        Run benchmarks:
-          $ python benchmarks.py --weights yolov5s.pt --img 640
-    """
-    y, t = [], time.time()
-    device = select_device(device)
-    model_type = type(attempt_load(weights, fuse=False))  # DetectionModel, SegmentationModel, etc.
-    for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, CPU, GPU)
-        try:
-            assert i not in (9, 10), "inference not supported"  # Edge TPU and TF.js are unsupported
-            assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13"  # CoreML
-            if "cpu" in device.type:
-                assert cpu, "inference not supported on CPU"
-            if "cuda" in device.type:
-                assert gpu, "inference not supported on GPU"
-
-            # Export
-            if f == "-":
-                w = weights  # PyTorch format
-            else:
-                w = export.run(
-                    weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half
-                )[-1]  # all others
-            assert suffix in str(w), "export failed"
-
-            # Validate
-            if model_type == SegmentationModel:
-                result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
-                metric = result[0][7]  # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
-            else:  # DetectionModel:
-                result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half)
-                metric = result[0][3]  # (p, r, map50, map, *loss(box, obj, cls))
-            speed = result[2][1]  # times (preprocess, inference, postprocess)
-            y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)])  # MB, mAP, t_inference
-        except Exception as e:
-            if hard_fail:
-                assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}"
-            LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}")
-            y.append([name, None, None, None])  # mAP, t_inference
-        if pt_only and i == 0:
-            break  # break after PyTorch
-
-    # Print results
-    LOGGER.info("\n")
-    parse_opt()
-    notebook_init()  # print system info
-    c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""]
-    py = pd.DataFrame(y, columns=c)
-    LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)")
-    LOGGER.info(str(py if map else py.iloc[:, :2]))
-    if hard_fail and isinstance(hard_fail, str):
-        metrics = py["mAP50-95"].array  # values to compare to floor
-        floor = eval(hard_fail)  # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
-        assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}"
-    return py
-
-
-def test(
-    weights=ROOT / "yolov5s.pt",  # weights path
-    imgsz=640,  # inference size (pixels)
-    batch_size=1,  # batch size
-    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
-    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    half=False,  # use FP16 half-precision inference
-    test=False,  # test exports only
-    pt_only=False,  # test PyTorch only
-    hard_fail=False,  # throw error on benchmark failure
-):
-    """
-    Run YOLOv5 export tests for all supported formats and log the results, including export statuses.
-
-    Args:
-        weights (Path | str): Path to the model weights file (.pt format). Default is 'ROOT / "yolov5s.pt"'.
-        imgsz (int): Inference image size (in pixels). Default is 640.
-        batch_size (int): Batch size for testing. Default is 1.
-        data (Path | str): Path to the dataset configuration file (.yaml format). Default is 'ROOT / "data/coco128.yaml"'.
-        device (str): Device for running the tests, can be 'cpu' or a specific CUDA device ('0', '0,1,2,3', etc.). Default is an empty string.
-        half (bool): Use FP16 half-precision for inference if True. Default is False.
-        test (bool): Test export formats only without running inference. Default is False.
-        pt_only (bool): Test only the PyTorch model if True. Default is False.
-        hard_fail (bool): Raise error on export or test failure if True. Default is False.
-
-    Returns:
-        pd.DataFrame: DataFrame containing the results of the export tests, including format names and export statuses.
-
-    Examples:
-        ```python
-        $ python benchmarks.py --weights yolov5s.pt --img 640
-        ```
-
-    Notes:
-        Supported export formats and models include PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow
-        SavedModel, TensorFlow GraphDef, TensorFlow Lite, and TensorFlow Edge TPU. Edge TPU and TF.js are unsupported.
-
-    Usage:
-        Install required packages:
-            $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU support
-            $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow   # GPU support
-            $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com  # TensorRT
-        Run export tests:
-            $ python benchmarks.py --weights yolov5s.pt --img 640
-    """
-    y, t = [], time.time()
-    device = select_device(device)
-    for i, (name, f, suffix, gpu) in export.export_formats().iterrows():  # index, (name, file, suffix, gpu-capable)
-        try:
-            w = (
-                weights
-                if f == "-"
-                else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1]
-            )  # weights
-            assert suffix in str(w), "export failed"
-            y.append([name, True])
-        except Exception:
-            y.append([name, False])  # mAP, t_inference
-
-    # Print results
-    LOGGER.info("\n")
-    parse_opt()
-    notebook_init()  # print system info
-    py = pd.DataFrame(y, columns=["Format", "Export"])
-    LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)")
-    LOGGER.info(str(py))
-    return py
-
-
-def parse_opt():
-    """
-    Parses command-line arguments for YOLOv5 model inference configuration.
-
-    Args:
-        weights (str): The path to the weights file. Defaults to 'ROOT / "yolov5s.pt"'.
-        imgsz (int): Inference size in pixels. Defaults to 640.
-        batch_size (int): Batch size. Defaults to 1.
-        data (str): Path to the dataset YAML file. Defaults to 'ROOT / "data/coco128.yaml"'.
-        device (str): CUDA device, e.g., '0' or '0,1,2,3' or 'cpu'. Defaults to an empty string (auto-select).
-        half (bool): Use FP16 half-precision inference. This is a flag and defaults to False.
-        test (bool): Test exports only. This is a flag and defaults to False.
-        pt_only (bool): Test PyTorch only. This is a flag and defaults to False.
-        hard_fail (bool | str): Throw an error on benchmark failure. Can be a boolean or a string representing a minimum
-            metric floor, e.g., '0.29'. Defaults to False.
-
-    Returns:
-        argparse.Namespace: Parsed command-line arguments encapsulated in an argparse Namespace object.
-
-    Notes:
-        The function modifies the 'opt.data' by checking and validating the YAML path using 'check_yaml()'.
-        The parsed arguments are printed for reference using 'print_args()'.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
-    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
-    parser.add_argument("--batch-size", type=int, default=1, help="batch size")
-    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
-    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
-    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
-    parser.add_argument("--test", action="store_true", help="test exports only")
-    parser.add_argument("--pt-only", action="store_true", help="test PyTorch only")
-    parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric")
-    opt = parser.parse_args()
-    opt.data = check_yaml(opt.data)  # check YAML
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    """
-    Executes YOLOv5 benchmark tests or main training/inference routines based on the provided command-line arguments.
-
-    Args:
-        opt (argparse.Namespace): Parsed command-line arguments including options for weights, image size, batch size, data
-            configuration, device, and other flags for inference settings.
-
-    Returns:
-        None: This function does not return any value. It leverages side-effects such as logging and running benchmarks.
-
-    Example:
-        ```python
-        if __name__ == "__main__":
-            opt = parse_opt()
-            main(opt)
-        ```
-
-    Notes:
-        - For a complete list of supported export formats and their respective requirements, refer to the
-          [Ultralytics YOLOv5 Export Formats](https://github.com/ultralytics/yolov5#export-formats).
-        - Ensure that you have installed all necessary dependencies by following the installation instructions detailed in
-          the [main repository](https://github.com/ultralytics/yolov5#installation).
-
-        ```shell
-        # Running benchmarks on default weights and image size
-        $ python benchmarks.py --weights yolov5s.pt --img 640
-        ```
-    """
-    test(**vars(opt)) if opt.test else run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

yolov5/classify/predict.py

@@ -1,241 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-"""
-Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
-
-Usage - sources:
-    $ python classify/predict.py --weights yolov5s-cls.pt --source 0                               # webcam
-                                                                   img.jpg                         # image
-                                                                   vid.mp4                         # video
-                                                                   screen                          # screenshot
-                                                                   path/                           # directory
-                                                                   list.txt                        # list of images
-                                                                   list.streams                    # list of streams
-                                                                   'path/*.jpg'                    # glob
-                                                                   'https://youtu.be/LNwODJXcvt4'  # YouTube
-                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-
-Usage - formats:
-    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
-                                           yolov5s-cls.torchscript        # TorchScript
-                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                           yolov5s-cls_openvino_model     # OpenVINO
-                                           yolov5s-cls.engine             # TensorRT
-                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
-                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
-                                           yolov5s-cls.pb                 # TensorFlow GraphDef
-                                           yolov5s-cls.tflite             # TensorFlow Lite
-                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
-                                           yolov5s-cls_paddle_model       # PaddlePaddle
-"""
-
-import argparse
-import os
-import platform
-import sys
-from pathlib import Path
-
-import torch
-import torch.nn.functional as F
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from ultralytics.utils.plotting import Annotator
-
-from models.common import DetectMultiBackend
-from utils.augmentations import classify_transforms
-from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
-from utils.general import (
-    LOGGER,
-    Profile,
-    check_file,
-    check_img_size,
-    check_imshow,
-    check_requirements,
-    colorstr,
-    cv2,
-    increment_path,
-    print_args,
-    strip_optimizer,
-)
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-@smart_inference_mode()
-def run(
-    weights=ROOT / "yolov5s-cls.pt",  # model.pt path(s)
-    source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)
-    data=ROOT / "data/coco128.yaml",  # dataset.yaml path
-    imgsz=(224, 224),  # inference size (height, width)
-    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    view_img=False,  # show results
-    save_txt=False,  # save results to *.txt
-    nosave=False,  # do not save images/videos
-    augment=False,  # augmented inference
-    visualize=False,  # visualize features
-    update=False,  # update all models
-    project=ROOT / "runs/predict-cls",  # save results to project/name
-    name="exp",  # save results to project/name
-    exist_ok=False,  # existing project/name ok, do not increment
-    half=False,  # use FP16 half-precision inference
-    dnn=False,  # use OpenCV DNN for ONNX inference
-    vid_stride=1,  # video frame-rate stride
-):
-    """Conducts YOLOv5 classification inference on diverse input sources and saves results."""
-    source = str(source)
-    save_img = not nosave and not source.endswith(".txt")  # save inference images
-    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
-    webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
-    screenshot = source.lower().startswith("screen")
-    if is_url and is_file:
-        source = check_file(source)  # download
-
-    # Directories
-    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-    (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
-    stride, names, pt = model.stride, model.names, model.pt
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Dataloader
-    bs = 1  # batch_size
-    if webcam:
-        view_img = check_imshow(warn=True)
-        dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
-        bs = len(dataset)
-    elif screenshot:
-        dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
-    else:
-        dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
-    vid_path, vid_writer = [None] * bs, [None] * bs
-
-    # Run inference
-    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
-    seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
-    for path, im, im0s, vid_cap, s in dataset:
-        with dt[0]:
-            im = torch.Tensor(im).to(model.device)
-            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
-            if len(im.shape) == 3:
-                im = im[None]  # expand for batch dim
-
-        # Inference
-        with dt[1]:
-            results = model(im)
-
-        # Post-process
-        with dt[2]:
-            pred = F.softmax(results, dim=1)  # probabilities
-
-        # Process predictions
-        for i, prob in enumerate(pred):  # per image
-            seen += 1
-            if webcam:  # batch_size >= 1
-                p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f"{i}: "
-            else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
-
-            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
-            annotator = Annotator(im0, example=str(names), pil=True)
-
-            # Print results
-            top5i = prob.argsort(0, descending=True)[:5].tolist()  # top 5 indices
-            s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
-
-            # Write results
-            text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i)
-            if save_img or view_img:  # Add bbox to image
-                annotator.text([32, 32], text, txt_color=(255, 255, 255))
-            if save_txt:  # Write to file
-                with open(f"{txt_path}.txt", "a") as f:
-                    f.write(text + "\n")
-
-            # Stream results
-            im0 = annotator.result()
-            if view_img:
-                if platform.system() == "Linux" and p not in windows:
-                    windows.append(p)
-                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
-                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == "image":
-                    cv2.imwrite(save_path, im0)
-                else:  # 'video' or 'stream'
-                    if vid_path[i] != save_path:  # new video
-                        vid_path[i] = save_path
-                        if isinstance(vid_writer[i], cv2.VideoWriter):
-                            vid_writer[i].release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                        save_path = str(Path(save_path).with_suffix(".mp4"))  # force *.mp4 suffix on results videos
-                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
-                    vid_writer[i].write(im0)
-
-        # Print time (inference-only)
-        LOGGER.info(f"{s}{dt[1].dt * 1e3:.1f}ms")
-
-    # Print results
-    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
-    LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
-    if save_txt or save_img:
-        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    if update:
-        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
-    """Parses command line arguments for YOLOv5 inference settings including model, source, device, and image size."""
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model path(s)")
-    parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
-    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
-    parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[224], help="inference size h,w")
-    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
-    parser.add_argument("--view-img", action="store_true", help="show results")
-    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
-    parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
-    parser.add_argument("--augment", action="store_true", help="augmented inference")
-    parser.add_argument("--visualize", action="store_true", help="visualize features")
-    parser.add_argument("--update", action="store_true", help="update all models")
-    parser.add_argument("--project", default=ROOT / "runs/predict-cls", help="save results to project/name")
-    parser.add_argument("--name", default="exp", help="save results to project/name")
-    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
-    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
-    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
-    parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    """Executes YOLOv5 model inference with options for ONNX DNN and video frame-rate stride adjustments."""
-    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
-    run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

yolov5/classify/train.py

@@ -1,382 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-"""
-Train a YOLOv5 classifier model on a classification dataset.
-
-Usage - Single-GPU training:
-    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224
-
-Usage - Multi-GPU DDP training:
-    $ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
-
-Datasets:           --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
-YOLOv5-cls models:  --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
-Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
-"""
-
-import argparse
-import os
-import subprocess
-import sys
-import time
-from copy import deepcopy
-from datetime import datetime
-from pathlib import Path
-
-import torch
-import torch.distributed as dist
-import torch.hub as hub
-import torch.optim.lr_scheduler as lr_scheduler
-import torchvision
-from torch.cuda import amp
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from classify import val as validate
-from models.experimental import attempt_load
-from models.yolo import ClassificationModel, DetectionModel
-from utils.dataloaders import create_classification_dataloader
-from utils.general import (
-    DATASETS_DIR,
-    LOGGER,
-    TQDM_BAR_FORMAT,
-    WorkingDirectory,
-    check_git_info,
-    check_git_status,
-    check_requirements,
-    colorstr,
-    download,
-    increment_path,
-    init_seeds,
-    print_args,
-    yaml_save,
-)
-from utils.loggers import GenericLogger
-from utils.plots import imshow_cls
-from utils.torch_utils import (
-    ModelEMA,
-    de_parallel,
-    model_info,
-    reshape_classifier_output,
-    select_device,
-    smart_DDP,
-    smart_optimizer,
-    smartCrossEntropyLoss,
-    torch_distributed_zero_first,
-)
-
-LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv("RANK", -1))
-WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
-GIT_INFO = check_git_info()
-
-
-def train(opt, device):
-    """Trains a YOLOv5 model, managing datasets, model optimization, logging, and saving checkpoints."""
-    init_seeds(opt.seed + 1 + RANK, deterministic=True)
-    save_dir, data, bs, epochs, nw, imgsz, pretrained = (
-        opt.save_dir,
-        Path(opt.data),
-        opt.batch_size,
-        opt.epochs,
-        min(os.cpu_count() - 1, opt.workers),
-        opt.imgsz,
-        str(opt.pretrained).lower() == "true",
-    )
-    cuda = device.type != "cpu"
-
-    # Directories
-    wdir = save_dir / "weights"
-    wdir.mkdir(parents=True, exist_ok=True)  # make dir
-    last, best = wdir / "last.pt", wdir / "best.pt"
-
-    # Save run settings
-    yaml_save(save_dir / "opt.yaml", vars(opt))
-
-    # Logger
-    logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
-
-    # Download Dataset
-    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
-        data_dir = data if data.is_dir() else (DATASETS_DIR / data)
-        if not data_dir.is_dir():
-            LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...")
-            t = time.time()
-            if str(data) == "imagenet":
-                subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True)
-            else:
-                url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{data}.zip"
-                download(url, dir=data_dir.parent)
-            s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
-            LOGGER.info(s)
-
-    # Dataloaders
-    nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()])  # number of classes
-    trainloader = create_classification_dataloader(
-        path=data_dir / "train",
-        imgsz=imgsz,
-        batch_size=bs // WORLD_SIZE,
-        augment=True,
-        cache=opt.cache,
-        rank=LOCAL_RANK,
-        workers=nw,
-    )
-
-    test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val"  # data/test or data/val
-    if RANK in {-1, 0}:
-        testloader = create_classification_dataloader(
-            path=test_dir,
-            imgsz=imgsz,
-            batch_size=bs // WORLD_SIZE * 2,
-            augment=False,
-            cache=opt.cache,
-            rank=-1,
-            workers=nw,
-        )
-
-    # Model
-    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
-        if Path(opt.model).is_file() or opt.model.endswith(".pt"):
-            model = attempt_load(opt.model, device="cpu", fuse=False)
-        elif opt.model in torchvision.models.__dict__:  # TorchVision models i.e. resnet50, efficientnet_b0
-            model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None)
-        else:
-            m = hub.list("ultralytics/yolov5")  # + hub.list('pytorch/vision')  # models
-            raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m))
-        if isinstance(model, DetectionModel):
-            LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
-            model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10)  # convert to classification model
-        reshape_classifier_output(model, nc)  # update class count
-    for m in model.modules():
-        if not pretrained and hasattr(m, "reset_parameters"):
-            m.reset_parameters()
-        if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
-            m.p = opt.dropout  # set dropout
-    for p in model.parameters():
-        p.requires_grad = True  # for training
-    model = model.to(device)
-
-    # Info
-    if RANK in {-1, 0}:
-        model.names = trainloader.dataset.classes  # attach class names
-        model.transforms = testloader.dataset.torch_transforms  # attach inference transforms
-        model_info(model)
-        if opt.verbose:
-            LOGGER.info(model)
-        images, labels = next(iter(trainloader))
-        file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg")
-        logger.log_images(file, name="Train Examples")
-        logger.log_graph(model, imgsz)  # log model
-
-    # Optimizer
-    optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)
-
-    # Scheduler
-    lrf = 0.01  # final lr (fraction of lr0)
-
-    # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf  # cosine
-    def lf(x):
-        """Linear learning rate scheduler function, scaling learning rate from initial value to `lrf` over `epochs`."""
-        return (1 - x / epochs) * (1 - lrf) + lrf  # linear
-
-    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
-    # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
-    #                                    final_div_factor=1 / 25 / lrf)
-
-    # EMA
-    ema = ModelEMA(model) if RANK in {-1, 0} else None
-
-    # DDP mode
-    if cuda and RANK != -1:
-        model = smart_DDP(model)
-
-    # Train
-    t0 = time.time()
-    criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing)  # loss function
-    best_fitness = 0.0
-    scaler = amp.GradScaler(enabled=cuda)
-    val = test_dir.stem  # 'val' or 'test'
-    LOGGER.info(
-        f"Image sizes {imgsz} train, {imgsz} test\n"
-        f"Using {nw * WORLD_SIZE} dataloader workers\n"
-        f"Logging results to {colorstr('bold', save_dir)}\n"
-        f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n"
-        f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
-    )
-    for epoch in range(epochs):  # loop over the dataset multiple times
-        tloss, vloss, fitness = 0.0, 0.0, 0.0  # train loss, val loss, fitness
-        model.train()
-        if RANK != -1:
-            trainloader.sampler.set_epoch(epoch)
-        pbar = enumerate(trainloader)
-        if RANK in {-1, 0}:
-            pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT)
-        for i, (images, labels) in pbar:  # progress bar
-            images, labels = images.to(device, non_blocking=True), labels.to(device)
-
-            # Forward
-            with amp.autocast(enabled=cuda):  # stability issues when enabled
-                loss = criterion(model(images), labels)
-
-            # Backward
-            scaler.scale(loss).backward()
-
-            # Optimize
-            scaler.unscale_(optimizer)  # unscale gradients
-            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)  # clip gradients
-            scaler.step(optimizer)
-            scaler.update()
-            optimizer.zero_grad()
-            if ema:
-                ema.update(model)
-
-            if RANK in {-1, 0}:
-                # Print
-                tloss = (tloss * i + loss.item()) / (i + 1)  # update mean losses
-                mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0)  # (GB)
-                pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36
-
-                # Test
-                if i == len(pbar) - 1:  # last batch
-                    top1, top5, vloss = validate.run(
-                        model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar
-                    )  # test accuracy, loss
-                    fitness = top1  # define fitness as top1 accuracy
-
-        # Scheduler
-        scheduler.step()
-
-        # Log metrics
-        if RANK in {-1, 0}:
-            # Best fitness
-            if fitness > best_fitness:
-                best_fitness = fitness
-
-            # Log
-            metrics = {
-                "train/loss": tloss,
-                f"{val}/loss": vloss,
-                "metrics/accuracy_top1": top1,
-                "metrics/accuracy_top5": top5,
-                "lr/0": optimizer.param_groups[0]["lr"],
-            }  # learning rate
-            logger.log_metrics(metrics, epoch)
-
-            # Save model
-            final_epoch = epoch + 1 == epochs
-            if (not opt.nosave) or final_epoch:
-                ckpt = {
-                    "epoch": epoch,
-                    "best_fitness": best_fitness,
-                    "model": deepcopy(ema.ema).half(),  # deepcopy(de_parallel(model)).half(),
-                    "ema": None,  # deepcopy(ema.ema).half(),
-                    "updates": ema.updates,
-                    "optimizer": None,  # optimizer.state_dict(),
-                    "opt": vars(opt),
-                    "git": GIT_INFO,  # {remote, branch, commit} if a git repo
-                    "date": datetime.now().isoformat(),
-                }
-
-                # Save last, best and delete
-                torch.save(ckpt, last)
-                if best_fitness == fitness:
-                    torch.save(ckpt, best)
-                del ckpt
-
-    # Train complete
-    if RANK in {-1, 0} and final_epoch:
-        LOGGER.info(
-            f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)"
-            f"\nResults saved to {colorstr('bold', save_dir)}"
-            f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
-            f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
-            f"\nExport:          python export.py --weights {best} --include onnx"
-            f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
-            f"\nVisualize:       https://netron.app\n"
-        )
-
-        # Plot examples
-        images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
-        pred = torch.max(ema.ema(images.to(device)), 1)[1]
-        file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg")
-
-        # Log results
-        meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
-        logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch)
-        logger.log_model(best, epochs, metadata=meta)
-
-
-def parse_opt(known=False):
-    """Parses command line arguments for YOLOv5 training including model path, dataset, epochs, and more, returning
-    parsed arguments.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path")
-    parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...")
-    parser.add_argument("--epochs", type=int, default=10, help="total training epochs")
-    parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs")
-    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)")
-    parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
-    parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"')
-    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
-    parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
-    parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name")
-    parser.add_argument("--name", default="exp", help="save to project/name")
-    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
-    parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False")
-    parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer")
-    parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate")
-    parser.add_argument("--decay", type=float, default=5e-5, help="weight decay")
-    parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon")
-    parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head")
-    parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)")
-    parser.add_argument("--verbose", action="store_true", help="Verbose mode")
-    parser.add_argument("--seed", type=int, default=0, help="Global training seed")
-    parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
-    return parser.parse_known_args()[0] if known else parser.parse_args()
-
-
-def main(opt):
-    """Executes YOLOv5 training with given options, handling device setup and DDP mode; includes pre-training checks."""
-    if RANK in {-1, 0}:
-        print_args(vars(opt))
-        check_git_status()
-        check_requirements(ROOT / "requirements.txt")
-
-    # DDP mode
-    device = select_device(opt.device, batch_size=opt.batch_size)
-    if LOCAL_RANK != -1:
-        assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size"
-        assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
-        assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
-        torch.cuda.set_device(LOCAL_RANK)
-        device = torch.device("cuda", LOCAL_RANK)
-        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
-
-    # Parameters
-    opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
-
-    # Train
-    train(opt, device)
-
-
-def run(**kwargs):
-    """
-    Executes YOLOv5 model training or inference with specified parameters, returning updated options.
-
-    Example: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
-    """
-    opt = parse_opt(True)
-    for k, v in kwargs.items():
-        setattr(opt, k, v)
-    main(opt)
-    return opt
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

yolov5/classify/tutorial.ipynb

@@ -1,1488 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "t6MPjfT5NrKQ"
-   },
-   "source": [
-    "<div align=\"center\">\n",
-    "\n",
-    "  <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
-    "    <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\"></a>\n",
-    "\n",
-    "\n",
-    "<br>\n",
-    "  <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a>\n",
-    "  <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
-    "  <a href=\"https://www.kaggle.com/models/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
-    "<br>\n",
-    "\n",
-    "This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>See <a href=\"https://github.com/ultralytics/yolov5/issues/new/choose\">GitHub</a> for community support or <a href=\"https://ultralytics.com/contact\">contact us</a> for professional support.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "7mGmQbAO5pQb"
-   },
-   "source": [
-    "# Setup\n",
-    "\n",
-    "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "wbvMlHd_QwMG",
-    "outputId": "0806e375-610d-4ec0-c867-763dbb518279"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!git clone https://github.com/ultralytics/yolov5  # clone\n",
-    "%cd yolov5\n",
-    "%pip install -qr requirements.txt  # install\n",
-    "\n",
-    "import torch\n",
-    "\n",
-    "import utils\n",
-    "\n",
-    "display = utils.notebook_init()  # checks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "4JnkELT0cIJg"
-   },
-   "source": [
-    "# 1. Predict\n",
-    "\n",
-    "`classify/predict.py` runs YOLOv5 Classification inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict-cls`. Example inference sources are:\n",
-    "\n",
-    "```shell\n",
-    "python classify/predict.py --source 0  # webcam\n",
-    "                              img.jpg  # image \n",
-    "                              vid.mp4  # video\n",
-    "                              screen  # screenshot\n",
-    "                              path/  # directory\n",
-    "                              'path/*.jpg'  # glob\n",
-    "                              'https://youtu.be/LNwODJXcvt4'  # YouTube\n",
-    "                              'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "zR9ZbuQCH7FX",
-    "outputId": "50504ef7-aa3e-4281-a4e3-d0c7df3c0ffe"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n",
-      "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
-      "\n",
-      "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\n",
-      "100% 10.5M/10.5M [00:00<00:00, 12.3MB/s]\n",
-      "\n",
-      "Fusing layers... \n",
-      "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
-      "image 1/2 /content/yolov5/data/images/bus.jpg: 224x224 minibus 0.39, police van 0.24, amphibious vehicle 0.05, recreational vehicle 0.04, trolleybus 0.03, 3.9ms\n",
-      "image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.6ms\n",
-      "Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\n",
-      "Results saved to \u001b[1mruns/predict-cls/exp\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python classify/predict.py --weights yolov5s-cls.pt --img 224 --source data/images\n",
-    "# display.Image(filename='runs/predict-cls/exp/zidane.jpg', width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hkAzDWJ7cWTr"
-   },
-   "source": [
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n",
-    "<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/202808393-50deb439-ae1b-4246-a685-7560c9b37211.jpg\" width=\"600\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0eq1SMWl6Sfn"
-   },
-   "source": [
-    "# 2. Validate\n",
-    "Validate a model's accuracy on the [Imagenet](https://image-net.org/) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "WQPtK1QYVaD_",
-    "outputId": "20fc0630-141e-4a90-ea06-342cbd7ce496"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2022-11-22 19:53:40--  https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n",
-      "Resolving image-net.org (image-net.org)... 171.64.68.16\n",
-      "Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 6744924160 (6.3G) [application/x-tar]\n",
-      "Saving to: ‘ILSVRC2012_img_val.tar’\n",
-      "\n",
-      "ILSVRC2012_img_val. 100%[===================>]   6.28G  16.1MB/s    in 10m 52s \n",
-      "\n",
-      "2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Download Imagenet val (6.3G, 50000 images)\n",
-    "!bash data/scripts/get_imagenet.sh --val"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "X58w8JLpMnjH",
-    "outputId": "41843132-98e2-4c25-d474-4cd7b246fb8e"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\n",
-      "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
-      "\n",
-      "Fusing layers... \n",
-      "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
-      "validating: 100% 391/391 [04:57<00:00,  1.31it/s]\n",
-      "                   Class      Images    top1_acc    top5_acc\n",
-      "                     all       50000       0.715       0.902\n",
-      "                   tench          50        0.94        0.98\n",
-      "                goldfish          50        0.88        0.92\n",
-      "       great white shark          50        0.78        0.96\n",
-      "             tiger shark          50        0.68        0.96\n",
-      "        hammerhead shark          50        0.82        0.92\n",
-      "            electric ray          50        0.76         0.9\n",
-      "                stingray          50         0.7         0.9\n",
-      "                    cock          50        0.78        0.92\n",
-      "                     hen          50        0.84        0.96\n",
-      "                 ostrich          50        0.98           1\n",
-      "               brambling          50         0.9        0.96\n",
-      "               goldfinch          50        0.92        0.98\n",
-      "             house finch          50        0.88        0.96\n",
-      "                   junco          50        0.94        0.98\n",
-      "          indigo bunting          50        0.86        0.88\n",
-      "          American robin          50         0.9        0.96\n",
-      "                  bulbul          50        0.84        0.96\n",
-      "                     jay          50         0.9        0.96\n",
-      "                  magpie          50        0.84        0.96\n",
-      "               chickadee          50         0.9           1\n",
-      "         American dipper          50        0.82        0.92\n",
-      "                    kite          50        0.76        0.94\n",
-      "              bald eagle          50        0.92           1\n",
-      "                 vulture          50        0.96           1\n",
-      "          great grey owl          50        0.94        0.98\n",
-      "         fire salamander          50        0.96        0.98\n",
-      "             smooth newt          50        0.58        0.94\n",
-      "                    newt          50        0.74         0.9\n",
-      "      spotted salamander          50        0.86        0.94\n",
-      "                 axolotl          50        0.86        0.96\n",
-      "       American bullfrog          50        0.78        0.92\n",
-      "               tree frog          50        0.84        0.96\n",
-      "             tailed frog          50        0.48         0.8\n",
-      "   loggerhead sea turtle          50        0.68        0.94\n",
-      "  leatherback sea turtle          50         0.5         0.8\n",
-      "              mud turtle          50        0.64        0.84\n",
-      "                terrapin          50        0.52        0.98\n",
-      "              box turtle          50        0.84        0.98\n",
-      "            banded gecko          50         0.7        0.88\n",
-      "            green iguana          50        0.76        0.94\n",
-      "          Carolina anole          50        0.58        0.96\n",
-      "desert grassland whiptail lizard          50        0.82        0.94\n",
-      "                   agama          50        0.74        0.92\n",
-      "   frilled-necked lizard          50        0.84        0.86\n",
-      "        alligator lizard          50        0.58        0.78\n",
-      "            Gila monster          50        0.72         0.8\n",
-      "   European green lizard          50        0.42         0.9\n",
-      "               chameleon          50        0.76        0.84\n",
-      "           Komodo dragon          50        0.86        0.96\n",
-      "          Nile crocodile          50         0.7        0.84\n",
-      "      American alligator          50        0.76        0.96\n",
-      "             triceratops          50         0.9        0.94\n",
-      "              worm snake          50        0.76        0.88\n",
-      "       ring-necked snake          50         0.8        0.92\n",
-      " eastern hog-nosed snake          50        0.58        0.88\n",
-      "      smooth green snake          50         0.6        0.94\n",
-      "               kingsnake          50        0.82         0.9\n",
-      "            garter snake          50        0.88        0.94\n",
-      "             water snake          50         0.7        0.94\n",
-      "              vine snake          50        0.66        0.76\n",
-      "             night snake          50        0.34        0.82\n",
-      "         boa constrictor          50         0.8        0.96\n",
-      "     African rock python          50        0.48        0.76\n",
-      "            Indian cobra          50        0.82        0.94\n",
-      "             green mamba          50        0.54        0.86\n",
-      "               sea snake          50        0.62         0.9\n",
-      "    Saharan horned viper          50        0.56        0.86\n",
-      "eastern diamondback rattlesnake          50         0.6        0.86\n",
-      "              sidewinder          50        0.28        0.86\n",
-      "               trilobite          50        0.98        0.98\n",
-      "              harvestman          50        0.86        0.94\n",
-      "                scorpion          50        0.86        0.94\n",
-      "    yellow garden spider          50        0.92        0.96\n",
-      "             barn spider          50        0.38        0.98\n",
-      "  European garden spider          50        0.62        0.98\n",
-      "    southern black widow          50        0.88        0.94\n",
-      "               tarantula          50        0.94           1\n",
-      "             wolf spider          50        0.82        0.92\n",
-      "                    tick          50        0.74        0.84\n",
-      "               centipede          50        0.68        0.82\n",
-      "            black grouse          50        0.88        0.98\n",
-      "               ptarmigan          50        0.78        0.94\n",
-      "           ruffed grouse          50        0.88           1\n",
-      "          prairie grouse          50        0.92           1\n",
-      "                 peacock          50        0.88         0.9\n",
-      "                   quail          50         0.9        0.94\n",
-      "               partridge          50        0.74        0.96\n",
-      "             grey parrot          50         0.9        0.96\n",
-      "                   macaw          50        0.88        0.98\n",
-      "sulphur-crested cockatoo          50        0.86        0.92\n",
-      "                lorikeet          50        0.96           1\n",
-      "                  coucal          50        0.82        0.88\n",
-      "               bee eater          50        0.96        0.98\n",
-      "                hornbill          50         0.9        0.96\n",
-      "             hummingbird          50        0.88        0.96\n",
-      "                 jacamar          50        0.92        0.94\n",
-      "                  toucan          50        0.84        0.94\n",
-      "                    duck          50        0.76        0.94\n",
-      "  red-breasted merganser          50        0.86        0.96\n",
-      "                   goose          50        0.74        0.96\n",
-      "              black swan          50        0.94        0.98\n",
-      "                  tusker          50        0.54        0.92\n",
-      "                 echidna          50        0.98           1\n",
-      "                platypus          50        0.72        0.84\n",
-      "                 wallaby          50        0.78        0.88\n",
-      "                   koala          50        0.84        0.92\n",
-      "                  wombat          50        0.78        0.84\n",
-      "               jellyfish          50        0.88        0.96\n",
-      "             sea anemone          50        0.72         0.9\n",
-      "             brain coral          50        0.88        0.96\n",
-      "                flatworm          50         0.8        0.98\n",
-      "                nematode          50        0.86         0.9\n",
-      "                   conch          50        0.74        0.88\n",
-      "                   snail          50        0.78        0.88\n",
-      "                    slug          50        0.74        0.82\n",
-      "                sea slug          50        0.88        0.98\n",
-      "                  chiton          50        0.88        0.98\n",
-      "      chambered nautilus          50        0.88        0.92\n",
-      "          Dungeness crab          50        0.78        0.94\n",
-      "               rock crab          50        0.68        0.86\n",
-      "            fiddler crab          50        0.64        0.86\n",
-      "           red king crab          50        0.76        0.96\n",
-      "        American lobster          50        0.78        0.96\n",
-      "           spiny lobster          50        0.74        0.88\n",
-      "                crayfish          50        0.56        0.86\n",
-      "             hermit crab          50        0.78        0.96\n",
-      "                  isopod          50        0.66        0.78\n",
-      "             white stork          50        0.88        0.96\n",
-      "             black stork          50        0.84        0.98\n",
-      "               spoonbill          50        0.96           1\n",
-      "                flamingo          50        0.94           1\n",
-      "       little blue heron          50        0.92        0.98\n",
-      "             great egret          50         0.9        0.96\n",
-      "                 bittern          50        0.86        0.94\n",
-      "            crane (bird)          50        0.62         0.9\n",
-      "                 limpkin          50        0.98           1\n",
-      "        common gallinule          50        0.92        0.96\n",
-      "           American coot          50         0.9        0.98\n",
-      "                 bustard          50        0.92        0.96\n",
-      "         ruddy turnstone          50        0.94           1\n",
-      "                  dunlin          50        0.86        0.94\n",
-      "         common redshank          50         0.9        0.96\n",
-      "               dowitcher          50        0.84        0.96\n",
-      "           oystercatcher          50        0.86        0.94\n",
-      "                 pelican          50        0.92        0.96\n",
-      "            king penguin          50        0.88        0.96\n",
-      "               albatross          50         0.9           1\n",
-      "              grey whale          50        0.84        0.92\n",
-      "            killer whale          50        0.92           1\n",
-      "                  dugong          50        0.84        0.96\n",
-      "                sea lion          50        0.82        0.92\n",
-      "               Chihuahua          50        0.66        0.84\n",
-      "           Japanese Chin          50        0.72        0.98\n",
-      "                 Maltese          50        0.76        0.94\n",
-      "               Pekingese          50        0.84        0.94\n",
-      "                Shih Tzu          50        0.74        0.96\n",
-      "    King Charles Spaniel          50        0.88        0.98\n",
-      "                Papillon          50        0.86        0.94\n",
-      "             toy terrier          50        0.48        0.94\n",
-      "     Rhodesian Ridgeback          50        0.76        0.98\n",
-      "            Afghan Hound          50        0.84           1\n",
-      "            Basset Hound          50         0.8        0.92\n",
-      "                  Beagle          50        0.82        0.96\n",
-      "              Bloodhound          50        0.48        0.72\n",
-      "      Bluetick Coonhound          50        0.86        0.94\n",
-      " Black and Tan Coonhound          50        0.54         0.8\n",
-      "Treeing Walker Coonhound          50        0.66        0.98\n",
-      "        English foxhound          50        0.32        0.84\n",
-      "       Redbone Coonhound          50        0.62        0.94\n",
-      "                  borzoi          50        0.92           1\n",
-      "         Irish Wolfhound          50        0.48        0.88\n",
-      "       Italian Greyhound          50        0.76        0.98\n",
-      "                 Whippet          50        0.74        0.92\n",
-      "            Ibizan Hound          50         0.6        0.86\n",
-      "      Norwegian Elkhound          50        0.88        0.98\n",
-      "              Otterhound          50        0.62         0.9\n",
-      "                  Saluki          50        0.72        0.92\n",
-      "      Scottish Deerhound          50        0.86        0.98\n",
-      "              Weimaraner          50        0.88        0.94\n",
-      "Staffordshire Bull Terrier          50        0.66        0.98\n",
-      "American Staffordshire Terrier          50        0.64        0.92\n",
-      "      Bedlington Terrier          50         0.9        0.92\n",
-      "          Border Terrier          50        0.86        0.92\n",
-      "      Kerry Blue Terrier          50        0.78        0.98\n",
-      "           Irish Terrier          50         0.7        0.96\n",
-      "         Norfolk Terrier          50        0.68         0.9\n",
-      "         Norwich Terrier          50        0.72           1\n",
-      "       Yorkshire Terrier          50        0.66         0.9\n",
-      "        Wire Fox Terrier          50        0.64        0.98\n",
-      "        Lakeland Terrier          50        0.74        0.92\n",
-      "        Sealyham Terrier          50        0.76         0.9\n",
-      "        Airedale Terrier          50        0.82        0.92\n",
-      "           Cairn Terrier          50        0.76         0.9\n",
-      "      Australian Terrier          50        0.48        0.84\n",
-      "  Dandie Dinmont Terrier          50        0.82        0.92\n",
-      "          Boston Terrier          50        0.92           1\n",
-      "     Miniature Schnauzer          50        0.68         0.9\n",
-      "         Giant Schnauzer          50        0.72        0.98\n",
-      "      Standard Schnauzer          50        0.74           1\n",
-      "        Scottish Terrier          50        0.76        0.96\n",
-      "         Tibetan Terrier          50        0.48           1\n",
-      "Australian Silky Terrier          50        0.66        0.96\n",
-      "Soft-coated Wheaten Terrier          50        0.74        0.96\n",
-      "West Highland White Terrier          50        0.88        0.96\n",
-      "              Lhasa Apso          50        0.68        0.96\n",
-      "   Flat-Coated Retriever          50        0.72        0.94\n",
-      "  Curly-coated Retriever          50        0.82        0.94\n",
-      "        Golden Retriever          50        0.86        0.94\n",
-      "      Labrador Retriever          50        0.82        0.94\n",
-      "Chesapeake Bay Retriever          50        0.76        0.96\n",
-      "German Shorthaired Pointer          50         0.8        0.96\n",
-      "                  Vizsla          50        0.68        0.96\n",
-      "          English Setter          50         0.7           1\n",
-      "            Irish Setter          50         0.8         0.9\n",
-      "           Gordon Setter          50        0.84        0.92\n",
-      "                Brittany          50        0.84        0.96\n",
-      "         Clumber Spaniel          50        0.92        0.96\n",
-      "English Springer Spaniel          50        0.88           1\n",
-      "  Welsh Springer Spaniel          50        0.92           1\n",
-      "         Cocker Spaniels          50         0.7        0.94\n",
-      "          Sussex Spaniel          50        0.72        0.92\n",
-      "     Irish Water Spaniel          50        0.88        0.98\n",
-      "                  Kuvasz          50        0.66         0.9\n",
-      "              Schipperke          50         0.9        0.98\n",
-      "             Groenendael          50         0.8        0.94\n",
-      "                Malinois          50        0.86        0.98\n",
-      "                  Briard          50        0.52         0.8\n",
-      "       Australian Kelpie          50         0.6        0.88\n",
-      "                Komondor          50        0.88        0.94\n",
-      "    Old English Sheepdog          50        0.94        0.98\n",
-      "       Shetland Sheepdog          50        0.74         0.9\n",
-      "                  collie          50         0.6        0.96\n",
-      "           Border Collie          50        0.74        0.96\n",
-      "    Bouvier des Flandres          50        0.78        0.94\n",
-      "              Rottweiler          50        0.88        0.96\n",
-      "     German Shepherd Dog          50         0.8        0.98\n",
-      "               Dobermann          50        0.68        0.96\n",
-      "      Miniature Pinscher          50        0.76        0.88\n",
-      "Greater Swiss Mountain Dog          50        0.68        0.94\n",
-      "    Bernese Mountain Dog          50        0.96           1\n",
-      "  Appenzeller Sennenhund          50        0.22           1\n",
-      "  Entlebucher Sennenhund          50        0.64        0.98\n",
-      "                   Boxer          50         0.7        0.92\n",
-      "             Bullmastiff          50        0.78        0.98\n",
-      "         Tibetan Mastiff          50        0.88        0.96\n",
-      "          French Bulldog          50        0.84        0.94\n",
-      "              Great Dane          50        0.54         0.9\n",
-      "             St. Bernard          50        0.92           1\n",
-      "                   husky          50        0.46        0.98\n",
-      "        Alaskan Malamute          50        0.76        0.96\n",
-      "          Siberian Husky          50        0.46        0.98\n",
-      "               Dalmatian          50        0.94        0.98\n",
-      "           Affenpinscher          50        0.78         0.9\n",
-      "                 Basenji          50        0.92        0.94\n",
-      "                     pug          50        0.94        0.98\n",
-      "              Leonberger          50           1           1\n",
-      "            Newfoundland          50        0.78        0.96\n",
-      "   Pyrenean Mountain Dog          50        0.78        0.96\n",
-      "                 Samoyed          50        0.96           1\n",
-      "              Pomeranian          50        0.98           1\n",
-      "               Chow Chow          50         0.9        0.96\n",
-      "                Keeshond          50        0.88        0.94\n",
-      "      Griffon Bruxellois          50        0.84        0.98\n",
-      "    Pembroke Welsh Corgi          50        0.82        0.94\n",
-      "    Cardigan Welsh Corgi          50        0.66        0.98\n",
-      "              Toy Poodle          50        0.52        0.88\n",
-      "        Miniature Poodle          50        0.52        0.92\n",
-      "         Standard Poodle          50         0.8           1\n",
-      "    Mexican hairless dog          50        0.88        0.98\n",
-      "               grey wolf          50        0.82        0.92\n",
-      "     Alaskan tundra wolf          50        0.78        0.98\n",
-      "                red wolf          50        0.48         0.9\n",
-      "                  coyote          50        0.64        0.86\n",
-      "                   dingo          50        0.76        0.88\n",
-      "                   dhole          50         0.9        0.98\n",
-      "        African wild dog          50        0.98           1\n",
-      "                   hyena          50        0.88        0.96\n",
-      "                 red fox          50        0.54        0.92\n",
-      "                 kit fox          50        0.72        0.98\n",
-      "              Arctic fox          50        0.94           1\n",
-      "                grey fox          50         0.7        0.94\n",
-      "               tabby cat          50        0.54        0.92\n",
-      "               tiger cat          50        0.22        0.94\n",
-      "             Persian cat          50         0.9        0.98\n",
-      "             Siamese cat          50        0.96           1\n",
-      "            Egyptian Mau          50        0.54         0.8\n",
-      "                  cougar          50         0.9           1\n",
-      "                    lynx          50        0.72        0.88\n",
-      "                 leopard          50        0.78        0.98\n",
-      "            snow leopard          50         0.9        0.98\n",
-      "                  jaguar          50         0.7        0.94\n",
-      "                    lion          50         0.9        0.98\n",
-      "                   tiger          50        0.92        0.98\n",
-      "                 cheetah          50        0.94        0.98\n",
-      "              brown bear          50        0.94        0.98\n",
-      "     American black bear          50         0.8           1\n",
-      "              polar bear          50        0.84        0.96\n",
-      "              sloth bear          50        0.72        0.92\n",
-      "                mongoose          50         0.7        0.92\n",
-      "                 meerkat          50        0.82        0.92\n",
-      "            tiger beetle          50        0.92        0.94\n",
-      "                 ladybug          50        0.86        0.94\n",
-      "           ground beetle          50        0.64        0.94\n",
-      "         longhorn beetle          50        0.62        0.88\n",
-      "             leaf beetle          50        0.64        0.98\n",
-      "             dung beetle          50        0.86        0.98\n",
-      "       rhinoceros beetle          50        0.86        0.94\n",
-      "                  weevil          50         0.9           1\n",
-      "                     fly          50        0.78        0.94\n",
-      "                     bee          50        0.68        0.94\n",
-      "                     ant          50        0.68        0.78\n",
-      "             grasshopper          50         0.5        0.92\n",
-      "                 cricket          50        0.64        0.92\n",
-      "            stick insect          50        0.64        0.92\n",
-      "               cockroach          50        0.72         0.8\n",
-      "                  mantis          50        0.64        0.86\n",
-      "                  cicada          50         0.9        0.96\n",
-      "              leafhopper          50        0.88        0.94\n",
-      "                lacewing          50        0.78        0.92\n",
-      "               dragonfly          50        0.82        0.98\n",
-      "               damselfly          50        0.82           1\n",
-      "             red admiral          50        0.94        0.96\n",
-      "                 ringlet          50        0.86        0.98\n",
-      "       monarch butterfly          50         0.9        0.92\n",
-      "             small white          50         0.9           1\n",
-      "       sulphur butterfly          50        0.92           1\n",
-      "gossamer-winged butterfly          50        0.88           1\n",
-      "                starfish          50        0.88        0.92\n",
-      "              sea urchin          50        0.84        0.94\n",
-      "            sea cucumber          50        0.66        0.84\n",
-      "       cottontail rabbit          50        0.72        0.94\n",
-      "                    hare          50        0.84        0.96\n",
-      "           Angora rabbit          50        0.94        0.98\n",
-      "                 hamster          50        0.96           1\n",
-      "               porcupine          50        0.88        0.98\n",
-      "            fox squirrel          50        0.76        0.94\n",
-      "                  marmot          50        0.92        0.96\n",
-      "                  beaver          50        0.78        0.94\n",
-      "              guinea pig          50        0.78        0.94\n",
-      "           common sorrel          50        0.96        0.98\n",
-      "                   zebra          50        0.94        0.96\n",
-      "                     pig          50         0.5        0.76\n",
-      "               wild boar          50        0.84        0.96\n",
-      "                 warthog          50        0.84        0.96\n",
-      "            hippopotamus          50        0.88        0.96\n",
-      "                      ox          50        0.48        0.94\n",
-      "           water buffalo          50        0.78        0.94\n",
-      "                   bison          50        0.88        0.96\n",
-      "                     ram          50        0.58        0.92\n",
-      "           bighorn sheep          50        0.66           1\n",
-      "             Alpine ibex          50        0.92        0.98\n",
-      "              hartebeest          50        0.94           1\n",
-      "                  impala          50        0.82        0.96\n",
-      "                 gazelle          50         0.7        0.96\n",
-      "               dromedary          50         0.9           1\n",
-      "                   llama          50        0.82        0.94\n",
-      "                  weasel          50        0.44        0.92\n",
-      "                    mink          50        0.78        0.96\n",
-      "        European polecat          50        0.46         0.9\n",
-      "     black-footed ferret          50        0.68        0.96\n",
-      "                   otter          50        0.66        0.88\n",
-      "                   skunk          50        0.96        0.96\n",
-      "                  badger          50        0.86        0.92\n",
-      "               armadillo          50        0.88         0.9\n",
-      "        three-toed sloth          50        0.96           1\n",
-      "               orangutan          50        0.78        0.92\n",
-      "                 gorilla          50        0.82        0.94\n",
-      "              chimpanzee          50        0.84        0.94\n",
-      "                  gibbon          50        0.76        0.86\n",
-      "                 siamang          50        0.68        0.94\n",
-      "                  guenon          50         0.8        0.94\n",
-      "            patas monkey          50        0.62        0.82\n",
-      "                  baboon          50         0.9        0.98\n",
-      "                 macaque          50         0.8        0.86\n",
-      "                  langur          50         0.6        0.82\n",
-      " black-and-white colobus          50        0.86         0.9\n",
-      "        proboscis monkey          50           1           1\n",
-      "                marmoset          50        0.74        0.98\n",
-      "   white-headed capuchin          50        0.72         0.9\n",
-      "           howler monkey          50        0.86        0.94\n",
-      "                    titi          50         0.5         0.9\n",
-      "Geoffroy's spider monkey          50        0.42         0.8\n",
-      "  common squirrel monkey          50        0.76        0.92\n",
-      "       ring-tailed lemur          50        0.72        0.94\n",
-      "                   indri          50         0.9        0.96\n",
-      "          Asian elephant          50        0.58        0.92\n",
-      "   African bush elephant          50         0.7        0.98\n",
-      "               red panda          50        0.94        0.94\n",
-      "             giant panda          50        0.94        0.98\n",
-      "                   snoek          50        0.74         0.9\n",
-      "                     eel          50         0.6        0.84\n",
-      "             coho salmon          50        0.84        0.96\n",
-      "             rock beauty          50        0.88        0.98\n",
-      "               clownfish          50        0.78        0.98\n",
-      "                sturgeon          50        0.68        0.94\n",
-      "                 garfish          50        0.62         0.8\n",
-      "                lionfish          50        0.96        0.96\n",
-      "              pufferfish          50        0.88        0.96\n",
-      "                  abacus          50        0.74        0.88\n",
-      "                   abaya          50        0.84        0.92\n",
-      "           academic gown          50        0.42        0.86\n",
-      "               accordion          50         0.8         0.9\n",
-      "         acoustic guitar          50         0.5        0.76\n",
-      "        aircraft carrier          50         0.8        0.96\n",
-      "                airliner          50        0.92           1\n",
-      "                 airship          50        0.76        0.82\n",
-      "                   altar          50        0.64        0.98\n",
-      "               ambulance          50        0.88        0.98\n",
-      "      amphibious vehicle          50        0.64        0.94\n",
-      "            analog clock          50        0.52        0.92\n",
-      "                  apiary          50        0.82        0.96\n",
-      "                   apron          50         0.7        0.84\n",
-      "         waste container          50         0.4         0.8\n",
-      "           assault rifle          50        0.42        0.84\n",
-      "                backpack          50        0.34        0.64\n",
-      "                  bakery          50         0.4        0.68\n",
-      "            balance beam          50         0.8        0.98\n",
-      "                 balloon          50        0.86        0.96\n",
-      "           ballpoint pen          50        0.52        0.96\n",
-      "                Band-Aid          50         0.7         0.9\n",
-      "                   banjo          50        0.84           1\n",
-      "                baluster          50        0.68        0.94\n",
-      "                 barbell          50        0.56         0.9\n",
-      "            barber chair          50         0.7        0.92\n",
-      "              barbershop          50        0.54        0.86\n",
-      "                    barn          50        0.96        0.96\n",
-      "               barometer          50        0.84        0.98\n",
-      "                  barrel          50        0.56        0.88\n",
-      "             wheelbarrow          50        0.66        0.88\n",
-      "                baseball          50        0.74        0.98\n",
-      "              basketball          50        0.88        0.98\n",
-      "                bassinet          50        0.66        0.92\n",
-      "                 bassoon          50        0.74        0.98\n",
-      "            swimming cap          50        0.62        0.88\n",
-      "              bath towel          50        0.54        0.78\n",
-      "                 bathtub          50         0.4        0.88\n",
-      "           station wagon          50        0.66        0.84\n",
-      "              lighthouse          50        0.78        0.94\n",
-      "                  beaker          50        0.52        0.68\n",
-      "            military cap          50        0.84        0.96\n",
-      "             beer bottle          50        0.66        0.88\n",
-      "              beer glass          50         0.6        0.84\n",
-      "                bell-cot          50        0.56        0.96\n",
-      "                     bib          50        0.58        0.82\n",
-      "          tandem bicycle          50        0.86        0.96\n",
-      "                  bikini          50        0.56        0.88\n",
-      "             ring binder          50        0.64        0.84\n",
-      "              binoculars          50        0.54        0.78\n",
-      "               birdhouse          50        0.86        0.94\n",
-      "               boathouse          50        0.74        0.92\n",
-      "               bobsleigh          50        0.92        0.96\n",
-      "                bolo tie          50         0.8        0.94\n",
-      "             poke bonnet          50        0.64        0.86\n",
-      "                bookcase          50        0.66        0.92\n",
-      "               bookstore          50        0.62        0.88\n",
-      "              bottle cap          50        0.58         0.7\n",
-      "                     bow          50        0.72        0.86\n",
-      "                 bow tie          50         0.7         0.9\n",
-      "                   brass          50        0.92        0.96\n",
-      "                     bra          50         0.5         0.7\n",
-      "              breakwater          50        0.62        0.86\n",
-      "             breastplate          50         0.4         0.9\n",
-      "                   broom          50         0.6        0.86\n",
-      "                  bucket          50        0.66         0.8\n",
-      "                  buckle          50         0.5        0.68\n",
-      "        bulletproof vest          50         0.5        0.78\n",
-      "        high-speed train          50        0.94        0.96\n",
-      "            butcher shop          50        0.74        0.94\n",
-      "                 taxicab          50        0.64        0.86\n",
-      "                cauldron          50        0.44        0.66\n",
-      "                  candle          50        0.48        0.74\n",
-      "                  cannon          50        0.88        0.94\n",
-      "                   canoe          50        0.94           1\n",
-      "              can opener          50        0.66        0.86\n",
-      "                cardigan          50        0.68         0.8\n",
-      "              car mirror          50        0.94        0.96\n",
-      "                carousel          50        0.94        0.98\n",
-      "                tool kit          50        0.56        0.78\n",
-      "                  carton          50        0.42         0.7\n",
-      "               car wheel          50        0.38        0.74\n",
-      "automated teller machine          50        0.76        0.94\n",
-      "                cassette          50        0.52         0.8\n",
-      "         cassette player          50        0.28         0.9\n",
-      "                  castle          50        0.78        0.88\n",
-      "               catamaran          50        0.78           1\n",
-      "               CD player          50        0.52        0.82\n",
-      "                   cello          50        0.82           1\n",
-      "            mobile phone          50        0.68        0.86\n",
-      "                   chain          50        0.38        0.66\n",
-      "        chain-link fence          50         0.7        0.84\n",
-      "              chain mail          50        0.64         0.9\n",
-      "                chainsaw          50        0.84        0.92\n",
-      "                   chest          50        0.68        0.92\n",
-      "              chiffonier          50        0.26        0.64\n",
-      "                   chime          50        0.62        0.84\n",
-      "           china cabinet          50        0.82        0.96\n",
-      "      Christmas stocking          50        0.92        0.94\n",
-      "                  church          50        0.62         0.9\n",
-      "           movie theater          50        0.58        0.88\n",
-      "                 cleaver          50        0.32        0.62\n",
-      "          cliff dwelling          50        0.88           1\n",
-      "                   cloak          50        0.32        0.64\n",
-      "                   clogs          50        0.58        0.88\n",
-      "         cocktail shaker          50        0.62         0.7\n",
-      "              coffee mug          50        0.44        0.72\n",
-      "             coffeemaker          50        0.64        0.92\n",
-      "                    coil          50        0.66        0.84\n",
-      "        combination lock          50        0.64        0.84\n",
-      "       computer keyboard          50         0.7        0.82\n",
-      "     confectionery store          50        0.54        0.86\n",
-      "          container ship          50        0.82        0.98\n",
-      "             convertible          50        0.78        0.98\n",
-      "               corkscrew          50        0.82        0.92\n",
-      "                  cornet          50        0.46        0.88\n",
-      "             cowboy boot          50        0.64         0.8\n",
-      "              cowboy hat          50        0.64        0.82\n",
-      "                  cradle          50        0.38         0.8\n",
-      "         crane (machine)          50        0.78        0.94\n",
-      "            crash helmet          50        0.92        0.96\n",
-      "                   crate          50        0.52        0.82\n",
-      "              infant bed          50        0.74           1\n",
-      "               Crock Pot          50        0.78         0.9\n",
-      "            croquet ball          50         0.9        0.96\n",
-      "                  crutch          50        0.46         0.7\n",
-      "                 cuirass          50        0.54        0.86\n",
-      "                     dam          50        0.74        0.92\n",
-      "                    desk          50         0.6        0.86\n",
-      "        desktop computer          50        0.54        0.94\n",
-      "   rotary dial telephone          50        0.88        0.94\n",
-      "                  diaper          50        0.68        0.84\n",
-      "           digital clock          50        0.54        0.76\n",
-      "           digital watch          50        0.58        0.86\n",
-      "            dining table          50        0.76         0.9\n",
-      "               dishcloth          50        0.94           1\n",
-      "              dishwasher          50        0.44        0.78\n",
-      "              disc brake          50        0.98           1\n",
-      "                    dock          50        0.54        0.94\n",
-      "                dog sled          50        0.84           1\n",
-      "                    dome          50        0.72        0.92\n",
-      "                 doormat          50        0.56        0.82\n",
-      "            drilling rig          50        0.84        0.96\n",
-      "                    drum          50        0.38        0.68\n",
-      "               drumstick          50        0.56        0.72\n",
-      "                dumbbell          50        0.62         0.9\n",
-      "              Dutch oven          50         0.7        0.84\n",
-      "            electric fan          50        0.82        0.86\n",
-      "         electric guitar          50        0.62        0.84\n",
-      "     electric locomotive          50        0.92        0.98\n",
-      "    entertainment center          50         0.9        0.98\n",
-      "                envelope          50        0.44        0.86\n",
-      "        espresso machine          50        0.72        0.94\n",
-      "             face powder          50         0.7        0.92\n",
-      "             feather boa          50         0.7        0.84\n",
-      "          filing cabinet          50        0.88        0.98\n",
-      "                fireboat          50        0.94        0.98\n",
-      "             fire engine          50        0.84         0.9\n",
-      "       fire screen sheet          50        0.62        0.76\n",
-      "                flagpole          50        0.74        0.88\n",
-      "                   flute          50        0.36        0.72\n",
-      "           folding chair          50        0.62        0.84\n",
-      "         football helmet          50        0.86        0.94\n",
-      "                forklift          50         0.8        0.92\n",
-      "                fountain          50        0.84        0.94\n",
-      "            fountain pen          50        0.76        0.92\n",
-      "         four-poster bed          50        0.78        0.94\n",
-      "             freight car          50        0.96           1\n",
-      "             French horn          50        0.76        0.92\n",
-      "              frying pan          50        0.36        0.78\n",
-      "                fur coat          50        0.84        0.96\n",
-      "           garbage truck          50         0.9        0.98\n",
-      "                gas mask          50        0.84        0.92\n",
-      "                gas pump          50         0.9        0.98\n",
-      "                  goblet          50        0.68        0.82\n",
-      "                 go-kart          50         0.9           1\n",
-      "               golf ball          50        0.84         0.9\n",
-      "               golf cart          50        0.78        0.86\n",
-      "                 gondola          50        0.98        0.98\n",
-      "                    gong          50        0.74        0.92\n",
-      "                    gown          50        0.62        0.96\n",
-      "             grand piano          50         0.7        0.96\n",
-      "              greenhouse          50         0.8        0.98\n",
-      "                  grille          50        0.72         0.9\n",
-      "           grocery store          50        0.66        0.94\n",
-      "              guillotine          50        0.86        0.92\n",
-      "                barrette          50        0.52        0.66\n",
-      "              hair spray          50         0.5        0.74\n",
-      "              half-track          50        0.78         0.9\n",
-      "                  hammer          50        0.56        0.76\n",
-      "                  hamper          50        0.64        0.84\n",
-      "              hair dryer          50        0.56        0.74\n",
-      "      hand-held computer          50        0.42        0.86\n",
-      "            handkerchief          50        0.78        0.94\n",
-      "         hard disk drive          50        0.76        0.84\n",
-      "               harmonica          50         0.7        0.88\n",
-      "                    harp          50        0.88        0.96\n",
-      "               harvester          50        0.78           1\n",
-      "                 hatchet          50        0.54        0.74\n",
-      "                 holster          50        0.66        0.84\n",
-      "            home theater          50        0.64        0.94\n",
-      "               honeycomb          50        0.56        0.88\n",
-      "                    hook          50         0.3         0.6\n",
-      "              hoop skirt          50        0.64        0.86\n",
-      "          horizontal bar          50        0.68        0.98\n",
-      "     horse-drawn vehicle          50        0.88        0.94\n",
-      "               hourglass          50        0.88        0.96\n",
-      "                    iPod          50        0.76        0.94\n",
-      "            clothes iron          50        0.82        0.88\n",
-      "         jack-o'-lantern          50        0.98        0.98\n",
-      "                   jeans          50        0.68        0.84\n",
-      "                    jeep          50        0.72         0.9\n",
-      "                 T-shirt          50        0.72        0.96\n",
-      "           jigsaw puzzle          50        0.84        0.94\n",
-      "         pulled rickshaw          50        0.86        0.94\n",
-      "                joystick          50         0.8         0.9\n",
-      "                  kimono          50        0.84        0.96\n",
-      "                knee pad          50        0.62        0.88\n",
-      "                    knot          50        0.66         0.8\n",
-      "                lab coat          50         0.8        0.96\n",
-      "                   ladle          50        0.36        0.64\n",
-      "               lampshade          50        0.48        0.84\n",
-      "         laptop computer          50        0.26        0.88\n",
-      "              lawn mower          50        0.78        0.96\n",
-      "                lens cap          50        0.46        0.72\n",
-      "             paper knife          50        0.26         0.5\n",
-      "                 library          50        0.54         0.9\n",
-      "                lifeboat          50        0.92        0.98\n",
-      "                 lighter          50        0.56        0.78\n",
-      "               limousine          50        0.76        0.92\n",
-      "             ocean liner          50        0.88        0.94\n",
-      "                lipstick          50        0.74         0.9\n",
-      "            slip-on shoe          50        0.74        0.92\n",
-      "                  lotion          50         0.5        0.86\n",
-      "                 speaker          50        0.52        0.68\n",
-      "                   loupe          50        0.32        0.52\n",
-      "                 sawmill          50        0.72         0.9\n",
-      "        magnetic compass          50        0.52        0.82\n",
-      "                mail bag          50        0.68        0.92\n",
-      "                 mailbox          50        0.82        0.92\n",
-      "                  tights          50        0.22        0.94\n",
-      "               tank suit          50        0.24         0.9\n",
-      "           manhole cover          50        0.96        0.98\n",
-      "                  maraca          50        0.74         0.9\n",
-      "                 marimba          50        0.84        0.94\n",
-      "                    mask          50        0.44        0.82\n",
-      "                   match          50        0.66         0.9\n",
-      "                 maypole          50        0.96           1\n",
-      "                    maze          50         0.8        0.96\n",
-      "           measuring cup          50        0.54        0.76\n",
-      "          medicine chest          50         0.6        0.84\n",
-      "                megalith          50         0.8        0.92\n",
-      "              microphone          50        0.52         0.7\n",
-      "          microwave oven          50        0.48        0.72\n",
-      "        military uniform          50        0.62        0.84\n",
-      "                milk can          50        0.68        0.82\n",
-      "                 minibus          50         0.7           1\n",
-      "               miniskirt          50        0.46        0.76\n",
-      "                 minivan          50        0.38         0.8\n",
-      "                 missile          50         0.4        0.84\n",
-      "                  mitten          50        0.76        0.88\n",
-      "             mixing bowl          50         0.8        0.92\n",
-      "             mobile home          50        0.54        0.78\n",
-      "                 Model T          50        0.92        0.96\n",
-      "                   modem          50        0.58        0.86\n",
-      "               monastery          50        0.44         0.9\n",
-      "                 monitor          50         0.4        0.86\n",
-      "                   moped          50        0.56        0.94\n",
-      "                  mortar          50        0.68        0.94\n",
-      "     square academic cap          50         0.5        0.84\n",
-      "                  mosque          50         0.9           1\n",
-      "            mosquito net          50         0.9        0.98\n",
-      "                 scooter          50         0.9        0.98\n",
-      "           mountain bike          50        0.78        0.96\n",
-      "                    tent          50        0.88        0.96\n",
-      "          computer mouse          50        0.42        0.82\n",
-      "               mousetrap          50        0.76        0.88\n",
-      "              moving van          50         0.4        0.72\n",
-      "                  muzzle          50         0.5        0.72\n",
-      "                    nail          50        0.68        0.74\n",
-      "              neck brace          50        0.56        0.68\n",
-      "                necklace          50        0.86           1\n",
-      "                  nipple          50         0.7        0.88\n",
-      "       notebook computer          50        0.34        0.84\n",
-      "                 obelisk          50         0.8        0.92\n",
-      "                    oboe          50         0.6        0.84\n",
-      "                 ocarina          50         0.8        0.86\n",
-      "                odometer          50        0.96           1\n",
-      "              oil filter          50        0.58        0.82\n",
-      "                   organ          50        0.82         0.9\n",
-      "            oscilloscope          50         0.9        0.96\n",
-      "               overskirt          50         0.2         0.7\n",
-      "            bullock cart          50         0.7        0.94\n",
-      "             oxygen mask          50        0.46        0.84\n",
-      "                  packet          50         0.5        0.78\n",
-      "                  paddle          50        0.56        0.94\n",
-      "            paddle wheel          50        0.86        0.96\n",
-      "                 padlock          50        0.74        0.78\n",
-      "              paintbrush          50        0.62         0.8\n",
-      "                 pajamas          50        0.56        0.92\n",
-      "                  palace          50        0.64        0.96\n",
-      "               pan flute          50        0.84        0.86\n",
-      "             paper towel          50        0.66        0.84\n",
-      "               parachute          50        0.92        0.94\n",
-      "           parallel bars          50        0.62        0.96\n",
-      "              park bench          50        0.74         0.9\n",
-      "           parking meter          50        0.84        0.92\n",
-      "           passenger car          50         0.5        0.82\n",
-      "                   patio          50        0.58        0.84\n",
-      "                payphone          50        0.74        0.92\n",
-      "                pedestal          50        0.52         0.9\n",
-      "             pencil case          50        0.64        0.92\n",
-      "        pencil sharpener          50        0.52        0.78\n",
-      "                 perfume          50         0.7         0.9\n",
-      "              Petri dish          50         0.6         0.8\n",
-      "             photocopier          50        0.88        0.98\n",
-      "                plectrum          50         0.7        0.84\n",
-      "             Pickelhaube          50        0.72        0.86\n",
-      "            picket fence          50        0.84        0.94\n",
-      "            pickup truck          50        0.64        0.92\n",
-      "                    pier          50        0.52        0.82\n",
-      "              piggy bank          50        0.82        0.94\n",
-      "             pill bottle          50        0.76        0.86\n",
-      "                  pillow          50        0.76         0.9\n",
-      "          ping-pong ball          50        0.84        0.88\n",
-      "                pinwheel          50        0.76        0.88\n",
-      "             pirate ship          50        0.76        0.94\n",
-      "                 pitcher          50        0.46        0.84\n",
-      "              hand plane          50        0.84        0.94\n",
-      "             planetarium          50        0.88        0.98\n",
-      "             plastic bag          50        0.36        0.62\n",
-      "              plate rack          50        0.52        0.78\n",
-      "                    plow          50        0.78        0.88\n",
-      "                 plunger          50        0.42         0.7\n",
-      "         Polaroid camera          50        0.84        0.92\n",
-      "                    pole          50        0.38        0.74\n",
-      "              police van          50        0.76        0.94\n",
-      "                  poncho          50        0.58        0.86\n",
-      "          billiard table          50         0.8        0.88\n",
-      "             soda bottle          50        0.56        0.94\n",
-      "                     pot          50        0.78        0.92\n",
-      "          potter's wheel          50         0.9        0.94\n",
-      "             power drill          50        0.42        0.72\n",
-      "              prayer rug          50         0.7        0.86\n",
-      "                 printer          50        0.54        0.86\n",
-      "                  prison          50         0.7         0.9\n",
-      "              projectile          50        0.28         0.9\n",
-      "               projector          50        0.62        0.84\n",
-      "             hockey puck          50        0.92        0.96\n",
-      "            punching bag          50         0.6        0.68\n",
-      "                   purse          50        0.42        0.78\n",
-      "                   quill          50        0.68        0.84\n",
-      "                   quilt          50        0.64         0.9\n",
-      "                race car          50        0.72        0.92\n",
-      "                  racket          50        0.72         0.9\n",
-      "                radiator          50        0.66        0.76\n",
-      "                   radio          50        0.64        0.92\n",
-      "         radio telescope          50         0.9        0.96\n",
-      "             rain barrel          50         0.8        0.98\n",
-      "    recreational vehicle          50        0.84        0.94\n",
-      "                    reel          50        0.72        0.82\n",
-      "           reflex camera          50        0.72        0.92\n",
-      "            refrigerator          50         0.7         0.9\n",
-      "          remote control          50         0.7        0.88\n",
-      "              restaurant          50         0.5        0.66\n",
-      "                revolver          50        0.82           1\n",
-      "                   rifle          50        0.38         0.7\n",
-      "           rocking chair          50        0.62        0.84\n",
-      "              rotisserie          50        0.88        0.92\n",
-      "                  eraser          50        0.54        0.76\n",
-      "              rugby ball          50        0.86        0.94\n",
-      "                   ruler          50        0.68        0.86\n",
-      "            running shoe          50        0.78        0.94\n",
-      "                    safe          50        0.82        0.92\n",
-      "              safety pin          50         0.4        0.62\n",
-      "             salt shaker          50        0.66         0.9\n",
-      "                  sandal          50        0.66        0.86\n",
-      "                  sarong          50        0.64        0.86\n",
-      "               saxophone          50        0.66        0.88\n",
-      "                scabbard          50        0.76        0.92\n",
-      "          weighing scale          50        0.58        0.78\n",
-      "              school bus          50        0.92           1\n",
-      "                schooner          50        0.84           1\n",
-      "              scoreboard          50         0.9        0.96\n",
-      "              CRT screen          50        0.14         0.7\n",
-      "                   screw          50         0.9        0.98\n",
-      "             screwdriver          50         0.3        0.58\n",
-      "               seat belt          50        0.88        0.94\n",
-      "          sewing machine          50        0.76         0.9\n",
-      "                  shield          50        0.56        0.82\n",
-      "              shoe store          50        0.78        0.96\n",
-      "                   shoji          50         0.8        0.92\n",
-      "         shopping basket          50        0.52        0.88\n",
-      "           shopping cart          50        0.76        0.92\n",
-      "                  shovel          50        0.62        0.84\n",
-      "              shower cap          50         0.7        0.84\n",
-      "          shower curtain          50        0.64        0.82\n",
-      "                     ski          50        0.74        0.92\n",
-      "                ski mask          50        0.72        0.88\n",
-      "            sleeping bag          50        0.68         0.8\n",
-      "              slide rule          50        0.72        0.88\n",
-      "            sliding door          50        0.44        0.78\n",
-      "            slot machine          50        0.94        0.98\n",
-      "                 snorkel          50        0.86        0.98\n",
-      "              snowmobile          50        0.88           1\n",
-      "                snowplow          50        0.84        0.98\n",
-      "          soap dispenser          50        0.56        0.86\n",
-      "             soccer ball          50        0.86        0.96\n",
-      "                    sock          50        0.62        0.76\n",
-      " solar thermal collector          50        0.72        0.96\n",
-      "                sombrero          50         0.6        0.84\n",
-      "               soup bowl          50        0.56        0.94\n",
-      "               space bar          50        0.34        0.88\n",
-      "            space heater          50        0.52        0.74\n",
-      "           space shuttle          50        0.82        0.96\n",
-      "                 spatula          50         0.3         0.6\n",
-      "               motorboat          50        0.86           1\n",
-      "              spider web          50         0.7         0.9\n",
-      "                 spindle          50        0.86        0.98\n",
-      "              sports car          50         0.6        0.94\n",
-      "               spotlight          50        0.26         0.6\n",
-      "                   stage          50        0.68        0.86\n",
-      "        steam locomotive          50        0.94           1\n",
-      "     through arch bridge          50        0.84        0.96\n",
-      "              steel drum          50        0.82         0.9\n",
-      "             stethoscope          50         0.6        0.82\n",
-      "                   scarf          50         0.5        0.92\n",
-      "              stone wall          50        0.76         0.9\n",
-      "               stopwatch          50        0.58         0.9\n",
-      "                   stove          50        0.46        0.74\n",
-      "                strainer          50        0.64        0.84\n",
-      "                    tram          50        0.88        0.96\n",
-      "               stretcher          50         0.6         0.8\n",
-      "                   couch          50         0.8        0.96\n",
-      "                   stupa          50        0.88        0.88\n",
-      "               submarine          50        0.72        0.92\n",
-      "                    suit          50         0.4        0.78\n",
-      "                 sundial          50        0.58        0.74\n",
-      "                sunglass          50        0.14        0.58\n",
-      "              sunglasses          50        0.28        0.58\n",
-      "               sunscreen          50        0.32         0.7\n",
-      "       suspension bridge          50         0.6        0.94\n",
-      "                     mop          50        0.74        0.92\n",
-      "              sweatshirt          50        0.28        0.66\n",
-      "                swimsuit          50        0.52        0.82\n",
-      "                   swing          50        0.76        0.84\n",
-      "                  switch          50        0.56        0.76\n",
-      "                 syringe          50        0.62        0.82\n",
-      "              table lamp          50         0.6        0.88\n",
-      "                    tank          50         0.8        0.96\n",
-      "             tape player          50        0.46        0.76\n",
-      "                  teapot          50        0.84           1\n",
-      "              teddy bear          50        0.82        0.94\n",
-      "              television          50         0.6         0.9\n",
-      "             tennis ball          50         0.7        0.94\n",
-      "           thatched roof          50        0.88         0.9\n",
-      "           front curtain          50         0.8        0.92\n",
-      "                 thimble          50         0.6         0.8\n",
-      "       threshing machine          50        0.56        0.88\n",
-      "                  throne          50        0.72        0.82\n",
-      "               tile roof          50        0.72        0.94\n",
-      "                 toaster          50        0.66        0.84\n",
-      "            tobacco shop          50        0.42         0.7\n",
-      "             toilet seat          50        0.62        0.88\n",
-      "                   torch          50        0.64        0.84\n",
-      "              totem pole          50        0.92        0.98\n",
-      "               tow truck          50        0.62        0.88\n",
-      "               toy store          50         0.6        0.94\n",
-      "                 tractor          50        0.76        0.98\n",
-      "      semi-trailer truck          50        0.78        0.92\n",
-      "                    tray          50        0.46        0.64\n",
-      "             trench coat          50        0.54        0.72\n",
-      "                tricycle          50        0.72        0.94\n",
-      "                trimaran          50         0.7        0.98\n",
-      "                  tripod          50        0.58        0.86\n",
-      "          triumphal arch          50        0.92        0.98\n",
-      "              trolleybus          50         0.9           1\n",
-      "                trombone          50        0.54        0.88\n",
-      "                     tub          50        0.24        0.82\n",
-      "               turnstile          50        0.84        0.94\n",
-      "     typewriter keyboard          50        0.68        0.98\n",
-      "                umbrella          50        0.52         0.7\n",
-      "                unicycle          50        0.74        0.96\n",
-      "           upright piano          50        0.76         0.9\n",
-      "          vacuum cleaner          50        0.62         0.9\n",
-      "                    vase          50         0.5        0.78\n",
-      "                   vault          50        0.76        0.92\n",
-      "                  velvet          50         0.2        0.42\n",
-      "         vending machine          50         0.9           1\n",
-      "                vestment          50        0.54        0.82\n",
-      "                 viaduct          50        0.78        0.86\n",
-      "                  violin          50        0.68        0.78\n",
-      "              volleyball          50        0.86           1\n",
-      "             waffle iron          50        0.72        0.88\n",
-      "              wall clock          50        0.54        0.88\n",
-      "                  wallet          50        0.52         0.9\n",
-      "                wardrobe          50        0.68        0.88\n",
-      "       military aircraft          50         0.9        0.98\n",
-      "                    sink          50        0.72        0.96\n",
-      "         washing machine          50        0.78        0.94\n",
-      "            water bottle          50        0.54        0.74\n",
-      "               water jug          50        0.22        0.74\n",
-      "             water tower          50         0.9        0.96\n",
-      "             whiskey jug          50        0.64        0.74\n",
-      "                 whistle          50        0.72        0.84\n",
-      "                     wig          50        0.84         0.9\n",
-      "           window screen          50        0.68         0.8\n",
-      "            window shade          50        0.52        0.76\n",
-      "             Windsor tie          50        0.22        0.66\n",
-      "             wine bottle          50        0.42        0.82\n",
-      "                    wing          50        0.54        0.96\n",
-      "                     wok          50        0.46        0.82\n",
-      "            wooden spoon          50        0.58         0.8\n",
-      "                    wool          50        0.32        0.82\n",
-      "        split-rail fence          50        0.74         0.9\n",
-      "               shipwreck          50        0.84        0.96\n",
-      "                    yawl          50        0.78        0.96\n",
-      "                    yurt          50        0.84           1\n",
-      "                 website          50        0.98           1\n",
-      "              comic book          50        0.62         0.9\n",
-      "               crossword          50        0.84        0.88\n",
-      "            traffic sign          50        0.78         0.9\n",
-      "           traffic light          50         0.8        0.94\n",
-      "             dust jacket          50        0.72        0.94\n",
-      "                    menu          50        0.82        0.96\n",
-      "                   plate          50        0.44        0.88\n",
-      "               guacamole          50         0.8        0.92\n",
-      "                consomme          50        0.54        0.88\n",
-      "                 hot pot          50        0.86        0.98\n",
-      "                  trifle          50        0.92        0.98\n",
-      "               ice cream          50        0.68        0.94\n",
-      "                 ice pop          50        0.62        0.84\n",
-      "                baguette          50        0.62        0.88\n",
-      "                   bagel          50        0.64        0.92\n",
-      "                 pretzel          50        0.72        0.88\n",
-      "            cheeseburger          50         0.9           1\n",
-      "                 hot dog          50        0.74        0.94\n",
-      "           mashed potato          50        0.74         0.9\n",
-      "                 cabbage          50        0.84        0.96\n",
-      "                broccoli          50         0.9        0.96\n",
-      "             cauliflower          50        0.82           1\n",
-      "                zucchini          50        0.74         0.9\n",
-      "        spaghetti squash          50         0.8        0.96\n",
-      "            acorn squash          50        0.82        0.96\n",
-      "        butternut squash          50         0.7        0.94\n",
-      "                cucumber          50         0.6        0.96\n",
-      "               artichoke          50        0.84        0.94\n",
-      "             bell pepper          50        0.84        0.98\n",
-      "                 cardoon          50        0.88        0.94\n",
-      "                mushroom          50        0.38        0.92\n",
-      "            Granny Smith          50         0.9        0.96\n",
-      "              strawberry          50         0.6        0.88\n",
-      "                  orange          50         0.7        0.92\n",
-      "                   lemon          50        0.78        0.98\n",
-      "                     fig          50        0.82        0.96\n",
-      "               pineapple          50        0.86        0.96\n",
-      "                  banana          50        0.84        0.96\n",
-      "               jackfruit          50         0.9        0.98\n",
-      "           custard apple          50        0.86        0.96\n",
-      "             pomegranate          50        0.82        0.98\n",
-      "                     hay          50         0.8        0.92\n",
-      "               carbonara          50        0.88        0.94\n",
-      "         chocolate syrup          50        0.46        0.84\n",
-      "                   dough          50         0.4         0.6\n",
-      "                meatloaf          50        0.58        0.84\n",
-      "                   pizza          50        0.84        0.96\n",
-      "                 pot pie          50        0.68         0.9\n",
-      "                 burrito          50         0.8        0.98\n",
-      "                red wine          50        0.54        0.82\n",
-      "                espresso          50        0.64        0.88\n",
-      "                     cup          50        0.38         0.7\n",
-      "                  eggnog          50        0.38         0.7\n",
-      "                     alp          50        0.54        0.88\n",
-      "                  bubble          50         0.8        0.96\n",
-      "                   cliff          50        0.64           1\n",
-      "              coral reef          50        0.72        0.96\n",
-      "                  geyser          50        0.94           1\n",
-      "               lakeshore          50        0.54        0.88\n",
-      "              promontory          50        0.58        0.94\n",
-      "                   shoal          50         0.6        0.96\n",
-      "                seashore          50        0.44        0.78\n",
-      "                  valley          50        0.72        0.94\n",
-      "                 volcano          50        0.78        0.96\n",
-      "         baseball player          50        0.72        0.94\n",
-      "              bridegroom          50        0.72        0.88\n",
-      "             scuba diver          50         0.8           1\n",
-      "                rapeseed          50        0.94        0.98\n",
-      "                   daisy          50        0.96        0.98\n",
-      "   yellow lady's slipper          50           1           1\n",
-      "                    corn          50         0.4        0.88\n",
-      "                   acorn          50        0.92        0.98\n",
-      "                rose hip          50        0.92        0.98\n",
-      "     horse chestnut seed          50        0.94        0.98\n",
-      "            coral fungus          50        0.96        0.96\n",
-      "                  agaric          50        0.82        0.94\n",
-      "               gyromitra          50        0.98           1\n",
-      "      stinkhorn mushroom          50         0.8        0.94\n",
-      "              earth star          50        0.98           1\n",
-      "        hen-of-the-woods          50         0.8        0.96\n",
-      "                  bolete          50        0.74        0.94\n",
-      "                     ear          50        0.48        0.94\n",
-      "            toilet paper          50        0.36        0.68\n",
-      "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n",
-      "Results saved to \u001b[1mruns/val-cls/exp\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Validate YOLOv5s on Imagenet val\n",
-    "!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZY2VXXXu74w5"
-   },
-   "source": [
-    "# 3. Train\n",
-    "\n",
-    "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\n",
-    "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
-    "<br><br>\n",
-    "\n",
-    "Train a YOLOv5s Classification model on the [Imagenette](https://image-net.org/) dataset with `--data imagenet`, starting from pretrained `--pretrained yolov5s-cls.pt`.\n",
-    "\n",
-    "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
-    "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
-    "- **Training Results** are saved to `runs/train-cls/` with incrementing run directories, i.e. `runs/train-cls/exp2`, `runs/train-cls/exp3` etc.\n",
-    "<br><br>\n",
-    "\n",
-    "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
-    "\n",
-    "## Train on Custom Data with Roboflow 🌟 NEW\n",
-    "\n",
-    "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
-    "\n",
-    "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-classification-custom-data/](https://blog.roboflow.com/train-yolov5-classification-custom-data/?ref=ultralytics)\n",
-    "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KZiKUAjtARHAfZCXbJRv14-pOnIsBLPV?usp=sharing)\n",
-    "<br>\n",
-    "\n",
-    "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://user-images.githubusercontent.com/26833433/202802162-92e60571-ab58-4409-948d-b31fddcd3c6f.png\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "i3oKtE4g-aNn"
-   },
-   "outputs": [],
-   "source": [
-    "# @title Select YOLOv5 🚀 logger {run: 'auto'}\n",
-    "logger = \"Comet\"  # @param ['Comet', 'ClearML', 'TensorBoard']\n",
-    "\n",
-    "if logger == \"Comet\":\n",
-    "    %pip install -q comet_ml\n",
-    "    import comet_ml\n",
-    "\n",
-    "    comet_ml.init()\n",
-    "elif logger == \"ClearML\":\n",
-    "    %pip install -q clearml\n",
-    "    import clearml\n",
-    "\n",
-    "    clearml.browser_login()\n",
-    "elif logger == \"TensorBoard\":\n",
-    "    %load_ext tensorboard\n",
-    "    %tensorboard --logdir runs/train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "1NcFxRcFdJ_O",
-    "outputId": "77c8d487-16db-4073-b3ea-06cabf2e7766"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n",
-      "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
-      "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
-      "\n",
-      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n",
-      "\n",
-      "Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\n",
-      "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenette160.zip to /content/datasets/imagenette160.zip...\n",
-      "100% 103M/103M [00:00<00:00, 347MB/s] \n",
-      "Unzipping /content/datasets/imagenette160.zip...\n",
-      "Dataset download success ✅ (3.3s), saved to \u001b[1m/content/datasets/imagenette160\u001b[0m\n",
-      "\n",
-      "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n",
-      "Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\n",
-      "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n",
-      "Image sizes 224 train, 224 test\n",
-      "Using 1 dataloader workers\n",
-      "Logging results to \u001b[1mruns/train-cls/exp\u001b[0m\n",
-      "Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\n",
-      "\n",
-      "     Epoch   GPU_mem  train_loss    val_loss    top1_acc    top5_acc\n",
-      "       1/5     1.47G        1.05       0.974       0.828       0.975: 100% 148/148 [00:38<00:00,  3.82it/s]\n",
-      "       2/5     1.73G       0.895       0.766       0.911       0.994: 100% 148/148 [00:36<00:00,  4.03it/s]\n",
-      "       3/5     1.73G        0.82       0.704       0.934       0.996: 100% 148/148 [00:35<00:00,  4.20it/s]\n",
-      "       4/5     1.73G       0.766       0.664       0.951       0.998: 100% 148/148 [00:36<00:00,  4.05it/s]\n",
-      "       5/5     1.73G       0.724       0.634       0.959       0.997: 100% 148/148 [00:37<00:00,  3.94it/s]\n",
-      "\n",
-      "Training complete (0.052 hours)\n",
-      "Results saved to \u001b[1mruns/train-cls/exp\u001b[0m\n",
-      "Predict:         python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n",
-      "Validate:        python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\n",
-      "Export:          python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\n",
-      "PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')\n",
-      "Visualize:       https://netron.app\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train YOLOv5s Classification on Imagenette160 for 3 epochs\n",
-    "!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "15glLzbQx5u0"
-   },
-   "source": [
-    "# 4. Visualize"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nWOsI5wJR1o3"
-   },
-   "source": [
-    "## Comet Logging and Visualization 🌟 NEW\n",
-    "\n",
-    "[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n",
-    "\n",
-    "Getting started is easy:\n",
-    "```shell\n",
-    "pip install comet_ml  # 1. install\n",
-    "export COMET_API_KEY=<Your API Key>  # 2. paste API key\n",
-    "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt  # 3. train\n",
-    "```\n",
-    "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
-    "\n",
-    "<a href=\"https://bit.ly/yolov5-readme-comet2\">\n",
-    "<img alt=\"Comet Dashboard\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\" width=\"1280\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Lay2WsTjNJzP"
-   },
-   "source": [
-    "## ClearML Logging and Automation 🌟 NEW\n",
-    "\n",
-    "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
-    "\n",
-    "- `pip install clearml`\n",
-    "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
-    "\n",
-    "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
-    "\n",
-    "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
-    "\n",
-    "<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
-    "<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-WPvRbS5Swl6"
-   },
-   "source": [
-    "## Local Logging\n",
-    "\n",
-    "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
-    "\n",
-    "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
-    "\n",
-    "<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Zelyeqbyt3GD"
-   },
-   "source": [
-    "# Environments\n",
-    "\n",
-    "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
-    "\n",
-    "- **Notebooks** with free GPU: <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a> <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/models/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
-    "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
-    "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
-    "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6Qu7Iesl0p54"
-   },
-   "source": [
-    "# Status\n",
-    "\n",
-    "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n",
-    "\n",
-    "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IEijrePND_2I"
-   },
-   "source": [
-    "# Appendix\n",
-    "\n",
-    "Additional content below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "GMusP4OAxFu6"
-   },
-   "outputs": [],
-   "source": [
-    "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
-    "\n",
-    "model = torch.hub.load(\n",
-    "    \"ultralytics/yolov5\", \"yolov5s\", force_reload=True, trust_repo=True\n",
-    ")  # or yolov5n - yolov5x6 or custom\n",
-    "im = \"https://ultralytics.com/images/zidane.jpg\"  # file, Path, PIL.Image, OpenCV, nparray, list\n",
-    "results = model(im)  # inference\n",
-    "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "name": "YOLOv5 Classification Tutorial",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

yolov5/classify/val.py

@@ -1,178 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-"""
-Validate a trained YOLOv5 classification model on a classification dataset.
-
-Usage:
-    $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
-    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate ImageNet
-
-Usage - formats:
-    $ python classify/val.py --weights yolov5s-cls.pt                 # PyTorch
-                                       yolov5s-cls.torchscript        # TorchScript
-                                       yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
-                                       yolov5s-cls_openvino_model     # OpenVINO
-                                       yolov5s-cls.engine             # TensorRT
-                                       yolov5s-cls.mlmodel            # CoreML (macOS-only)
-                                       yolov5s-cls_saved_model        # TensorFlow SavedModel
-                                       yolov5s-cls.pb                 # TensorFlow GraphDef
-                                       yolov5s-cls.tflite             # TensorFlow Lite
-                                       yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
-                                       yolov5s-cls_paddle_model       # PaddlePaddle
-"""
-
-import argparse
-import os
-import sys
-from pathlib import Path
-
-import torch
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import create_classification_dataloader
-from utils.general import (
-    LOGGER,
-    TQDM_BAR_FORMAT,
-    Profile,
-    check_img_size,
-    check_requirements,
-    colorstr,
-    increment_path,
-    print_args,
-)
-from utils.torch_utils import select_device, smart_inference_mode
-
-
-@smart_inference_mode()
-def run(
-    data=ROOT / "../datasets/mnist",  # dataset dir
-    weights=ROOT / "yolov5s-cls.pt",  # model.pt path(s)
-    batch_size=128,  # batch size
-    imgsz=224,  # inference size (pixels)
-    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    workers=8,  # max dataloader workers (per RANK in DDP mode)
-    verbose=False,  # verbose output
-    project=ROOT / "runs/val-cls",  # save to project/name
-    name="exp",  # save to project/name
-    exist_ok=False,  # existing project/name ok, do not increment
-    half=False,  # use FP16 half-precision inference
-    dnn=False,  # use OpenCV DNN for ONNX inference
-    model=None,
-    dataloader=None,
-    criterion=None,
-    pbar=None,
-):
-    """Validates a YOLOv5 classification model on a dataset, computing metrics like top1 and top5 accuracy."""
-    # Initialize/load model and set device
-    training = model is not None
-    if training:  # called by train.py
-        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
-        half &= device.type != "cpu"  # half precision only supported on CUDA
-        model.half() if half else model.float()
-    else:  # called directly
-        device = select_device(device, batch_size=batch_size)
-
-        # Directories
-        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-        save_dir.mkdir(parents=True, exist_ok=True)  # make dir
-
-        # Load model
-        model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
-        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
-        imgsz = check_img_size(imgsz, s=stride)  # check image size
-        half = model.fp16  # FP16 supported on limited backends with CUDA
-        if engine:
-            batch_size = model.batch_size
-        else:
-            device = model.device
-            if not (pt or jit):
-                batch_size = 1  # export.py models default to batch-size 1
-                LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
-
-        # Dataloader
-        data = Path(data)
-        test_dir = data / "test" if (data / "test").exists() else data / "val"  # data/test or data/val
-        dataloader = create_classification_dataloader(
-            path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers
-        )
-
-    model.eval()
-    pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device))
-    n = len(dataloader)  # number of batches
-    action = "validating" if dataloader.dataset.root.stem == "val" else "testing"
-    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
-    bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
-    with torch.cuda.amp.autocast(enabled=device.type != "cpu"):
-        for images, labels in bar:
-            with dt[0]:
-                images, labels = images.to(device, non_blocking=True), labels.to(device)
-
-            with dt[1]:
-                y = model(images)
-
-            with dt[2]:
-                pred.append(y.argsort(1, descending=True)[:, :5])
-                targets.append(labels)
-                if criterion:
-                    loss += criterion(y, labels)
-
-    loss /= n
-    pred, targets = torch.cat(pred), torch.cat(targets)
-    correct = (targets[:, None] == pred).float()
-    acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy
-    top1, top5 = acc.mean(0).tolist()
-
-    if pbar:
-        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
-    if verbose:  # all classes
-        LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
-        LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
-        for i, c in model.names.items():
-            acc_i = acc[targets == i]
-            top1i, top5i = acc_i.mean(0).tolist()
-            LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
-
-        # Print results
-        t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt)  # speeds per image
-        shape = (1, 3, imgsz, imgsz)
-        LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t)
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
-
-    return top1, top5, loss
-
-
-def parse_opt():
-    """Parses and returns command line arguments for YOLOv5 model evaluation and inference settings."""
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path")
-    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)")
-    parser.add_argument("--batch-size", type=int, default=128, help="batch size")
-    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)")
-    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
-    parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
-    parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output")
-    parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name")
-    parser.add_argument("--name", default="exp", help="save to project/name")
-    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
-    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
-    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
-    opt = parser.parse_args()
-    print_args(vars(opt))
-    return opt
-
-
-def main(opt):
-    """Executes the YOLOv5 model prediction workflow, handling argument parsing and requirement checks."""
-    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
-    run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

yolov5/data/Argoverse.yaml

@@ -1,73 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
-# Example usage: python train.py --data Argoverse.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── Argoverse  ← downloads here (31.3 GB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Argoverse # dataset root dir
-train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
-val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
-test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
-
-# Classes
-names:
-  0: person
-  1: bicycle
-  2: car
-  3: motorcycle
-  4: bus
-  5: truck
-  6: traffic_light
-  7: stop_sign
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import json
-
-  from tqdm import tqdm
-  from utils.general import download, Path
-
-
-  def argoverse2yolo(set):
-      labels = {}
-      a = json.load(open(set, "rb"))
-      for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
-          img_id = annot['image_id']
-          img_name = a['images'][img_id]['name']
-          img_label_name = f'{img_name[:-3]}txt'
-
-          cls = annot['category_id']  # instance class id
-          x_center, y_center, width, height = annot['bbox']
-          x_center = (x_center + width / 2) / 1920.0  # offset and scale
-          y_center = (y_center + height / 2) / 1200.0  # offset and scale
-          width /= 1920.0  # scale
-          height /= 1200.0  # scale
-
-          img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
-          if not img_dir.exists():
-              img_dir.mkdir(parents=True, exist_ok=True)
-
-          k = str(img_dir / img_label_name)
-          if k not in labels:
-              labels[k] = []
-          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
-
-      for k in labels:
-          with open(k, "w") as f:
-              f.writelines(labels[k])
-
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
-  download(urls, dir=dir, delete=False)
-
-  # Convert
-  annotations_dir = 'Argoverse-HD/annotations/'
-  (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images')  # rename 'tracking' to 'images'
-  for d in "train.json", "val.json":
-      argoverse2yolo(dir / annotations_dir / d)  # convert VisDrone annotations to YOLO labels

yolov5/data/GlobalWheat2020.yaml

@@ -1,53 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
-# Example usage: python train.py --data GlobalWheat2020.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── GlobalWheat2020  ← downloads here (7.0 GB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/GlobalWheat2020 # dataset root dir
-train: # train images (relative to 'path') 3422 images
-  - images/arvalis_1
-  - images/arvalis_2
-  - images/arvalis_3
-  - images/ethz_1
-  - images/rres_1
-  - images/inrae_1
-  - images/usask_1
-val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
-  - images/ethz_1
-test: # test images (optional) 1276 images
-  - images/utokyo_1
-  - images/utokyo_2
-  - images/nau_1
-  - images/uq_1
-
-# Classes
-names:
-  0: wheat_head
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  from utils.general import download, Path
-
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
-          'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
-  download(urls, dir=dir)
-
-  # Make Directories
-  for p in 'annotations', 'images', 'labels':
-      (dir / p).mkdir(parents=True, exist_ok=True)
-
-  # Move
-  for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
-           'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
-      (dir / p).rename(dir / 'images' / p)  # move to /images
-      f = (dir / p).with_suffix('.json')  # json file
-      if f.exists():
-          f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations

yolov5/data/ImageNet.yaml

@@ -1,1021 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
-# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
-# Example usage: python classify/train.py --data imagenet
-# parent
-# ├── yolov5
-# └── datasets
-#     └── imagenet  ← downloads here (144 GB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/imagenet # dataset root dir
-train: train # train images (relative to 'path') 1281167 images
-val: val # val images (relative to 'path') 50000 images
-test: # test images (optional)
-
-# Classes
-names:
-  0: tench
-  1: goldfish
-  2: great white shark
-  3: tiger shark
-  4: hammerhead shark
-  5: electric ray
-  6: stingray
-  7: cock
-  8: hen
-  9: ostrich
-  10: brambling
-  11: goldfinch
-  12: house finch
-  13: junco
-  14: indigo bunting
-  15: American robin
-  16: bulbul
-  17: jay
-  18: magpie
-  19: chickadee
-  20: American dipper
-  21: kite
-  22: bald eagle
-  23: vulture
-  24: great grey owl
-  25: fire salamander
-  26: smooth newt
-  27: newt
-  28: spotted salamander
-  29: axolotl
-  30: American bullfrog
-  31: tree frog
-  32: tailed frog
-  33: loggerhead sea turtle
-  34: leatherback sea turtle
-  35: mud turtle
-  36: terrapin
-  37: box turtle
-  38: banded gecko
-  39: green iguana
-  40: Carolina anole
-  41: desert grassland whiptail lizard
-  42: agama
-  43: frilled-necked lizard
-  44: alligator lizard
-  45: Gila monster
-  46: European green lizard
-  47: chameleon
-  48: Komodo dragon
-  49: Nile crocodile
-  50: American alligator
-  51: triceratops
-  52: worm snake
-  53: ring-necked snake
-  54: eastern hog-nosed snake
-  55: smooth green snake
-  56: kingsnake
-  57: garter snake
-  58: water snake
-  59: vine snake
-  60: night snake
-  61: boa constrictor
-  62: African rock python
-  63: Indian cobra
-  64: green mamba
-  65: sea snake
-  66: Saharan horned viper
-  67: eastern diamondback rattlesnake
-  68: sidewinder
-  69: trilobite
-  70: harvestman
-  71: scorpion
-  72: yellow garden spider
-  73: barn spider
-  74: European garden spider
-  75: southern black widow
-  76: tarantula
-  77: wolf spider
-  78: tick
-  79: centipede
-  80: black grouse
-  81: ptarmigan
-  82: ruffed grouse
-  83: prairie grouse
-  84: peacock
-  85: quail
-  86: partridge
-  87: grey parrot
-  88: macaw
-  89: sulphur-crested cockatoo
-  90: lorikeet
-  91: coucal
-  92: bee eater
-  93: hornbill
-  94: hummingbird
-  95: jacamar
-  96: toucan
-  97: duck
-  98: red-breasted merganser
-  99: goose
-  100: black swan
-  101: tusker
-  102: echidna
-  103: platypus
-  104: wallaby
-  105: koala
-  106: wombat
-  107: jellyfish
-  108: sea anemone
-  109: brain coral
-  110: flatworm
-  111: nematode
-  112: conch
-  113: snail
-  114: slug
-  115: sea slug
-  116: chiton
-  117: chambered nautilus
-  118: Dungeness crab
-  119: rock crab
-  120: fiddler crab
-  121: red king crab
-  122: American lobster
-  123: spiny lobster
-  124: crayfish
-  125: hermit crab
-  126: isopod
-  127: white stork
-  128: black stork
-  129: spoonbill
-  130: flamingo
-  131: little blue heron
-  132: great egret
-  133: bittern
-  134: crane (bird)
-  135: limpkin
-  136: common gallinule
-  137: American coot
-  138: bustard
-  139: ruddy turnstone
-  140: dunlin
-  141: common redshank
-  142: dowitcher
-  143: oystercatcher
-  144: pelican
-  145: king penguin
-  146: albatross
-  147: grey whale
-  148: killer whale
-  149: dugong
-  150: sea lion
-  151: Chihuahua
-  152: Japanese Chin
-  153: Maltese
-  154: Pekingese
-  155: Shih Tzu
-  156: King Charles Spaniel
-  157: Papillon
-  158: toy terrier
-  159: Rhodesian Ridgeback
-  160: Afghan Hound
-  161: Basset Hound
-  162: Beagle
-  163: Bloodhound
-  164: Bluetick Coonhound
-  165: Black and Tan Coonhound
-  166: Treeing Walker Coonhound
-  167: English foxhound
-  168: Redbone Coonhound
-  169: borzoi
-  170: Irish Wolfhound
-  171: Italian Greyhound
-  172: Whippet
-  173: Ibizan Hound
-  174: Norwegian Elkhound
-  175: Otterhound
-  176: Saluki
-  177: Scottish Deerhound
-  178: Weimaraner
-  179: Staffordshire Bull Terrier
-  180: American Staffordshire Terrier
-  181: Bedlington Terrier
-  182: Border Terrier
-  183: Kerry Blue Terrier
-  184: Irish Terrier
-  185: Norfolk Terrier
-  186: Norwich Terrier
-  187: Yorkshire Terrier
-  188: Wire Fox Terrier
-  189: Lakeland Terrier
-  190: Sealyham Terrier
-  191: Airedale Terrier
-  192: Cairn Terrier
-  193: Australian Terrier
-  194: Dandie Dinmont Terrier
-  195: Boston Terrier
-  196: Miniature Schnauzer
-  197: Giant Schnauzer
-  198: Standard Schnauzer
-  199: Scottish Terrier
-  200: Tibetan Terrier
-  201: Australian Silky Terrier
-  202: Soft-coated Wheaten Terrier
-  203: West Highland White Terrier
-  204: Lhasa Apso
-  205: Flat-Coated Retriever
-  206: Curly-coated Retriever
-  207: Golden Retriever
-  208: Labrador Retriever
-  209: Chesapeake Bay Retriever
-  210: German Shorthaired Pointer
-  211: Vizsla
-  212: English Setter
-  213: Irish Setter
-  214: Gordon Setter
-  215: Brittany
-  216: Clumber Spaniel
-  217: English Springer Spaniel
-  218: Welsh Springer Spaniel
-  219: Cocker Spaniels
-  220: Sussex Spaniel
-  221: Irish Water Spaniel
-  222: Kuvasz
-  223: Schipperke
-  224: Groenendael
-  225: Malinois
-  226: Briard
-  227: Australian Kelpie
-  228: Komondor
-  229: Old English Sheepdog
-  230: Shetland Sheepdog
-  231: collie
-  232: Border Collie
-  233: Bouvier des Flandres
-  234: Rottweiler
-  235: German Shepherd Dog
-  236: Dobermann
-  237: Miniature Pinscher
-  238: Greater Swiss Mountain Dog
-  239: Bernese Mountain Dog
-  240: Appenzeller Sennenhund
-  241: Entlebucher Sennenhund
-  242: Boxer
-  243: Bullmastiff
-  244: Tibetan Mastiff
-  245: French Bulldog
-  246: Great Dane
-  247: St. Bernard
-  248: husky
-  249: Alaskan Malamute
-  250: Siberian Husky
-  251: Dalmatian
-  252: Affenpinscher
-  253: Basenji
-  254: pug
-  255: Leonberger
-  256: Newfoundland
-  257: Pyrenean Mountain Dog
-  258: Samoyed
-  259: Pomeranian
-  260: Chow Chow
-  261: Keeshond
-  262: Griffon Bruxellois
-  263: Pembroke Welsh Corgi
-  264: Cardigan Welsh Corgi
-  265: Toy Poodle
-  266: Miniature Poodle
-  267: Standard Poodle
-  268: Mexican hairless dog
-  269: grey wolf
-  270: Alaskan tundra wolf
-  271: red wolf
-  272: coyote
-  273: dingo
-  274: dhole
-  275: African wild dog
-  276: hyena
-  277: red fox
-  278: kit fox
-  279: Arctic fox
-  280: grey fox
-  281: tabby cat
-  282: tiger cat
-  283: Persian cat
-  284: Siamese cat
-  285: Egyptian Mau
-  286: cougar
-  287: lynx
-  288: leopard
-  289: snow leopard
-  290: jaguar
-  291: lion
-  292: tiger
-  293: cheetah
-  294: brown bear
-  295: American black bear
-  296: polar bear
-  297: sloth bear
-  298: mongoose
-  299: meerkat
-  300: tiger beetle
-  301: ladybug
-  302: ground beetle
-  303: longhorn beetle
-  304: leaf beetle
-  305: dung beetle
-  306: rhinoceros beetle
-  307: weevil
-  308: fly
-  309: bee
-  310: ant
-  311: grasshopper
-  312: cricket
-  313: stick insect
-  314: cockroach
-  315: mantis
-  316: cicada
-  317: leafhopper
-  318: lacewing
-  319: dragonfly
-  320: damselfly
-  321: red admiral
-  322: ringlet
-  323: monarch butterfly
-  324: small white
-  325: sulphur butterfly
-  326: gossamer-winged butterfly
-  327: starfish
-  328: sea urchin
-  329: sea cucumber
-  330: cottontail rabbit
-  331: hare
-  332: Angora rabbit
-  333: hamster
-  334: porcupine
-  335: fox squirrel
-  336: marmot
-  337: beaver
-  338: guinea pig
-  339: common sorrel
-  340: zebra
-  341: pig
-  342: wild boar
-  343: warthog
-  344: hippopotamus
-  345: ox
-  346: water buffalo
-  347: bison
-  348: ram
-  349: bighorn sheep
-  350: Alpine ibex
-  351: hartebeest
-  352: impala
-  353: gazelle
-  354: dromedary
-  355: llama
-  356: weasel
-  357: mink
-  358: European polecat
-  359: black-footed ferret
-  360: otter
-  361: skunk
-  362: badger
-  363: armadillo
-  364: three-toed sloth
-  365: orangutan
-  366: gorilla
-  367: chimpanzee
-  368: gibbon
-  369: siamang
-  370: guenon
-  371: patas monkey
-  372: baboon
-  373: macaque
-  374: langur
-  375: black-and-white colobus
-  376: proboscis monkey
-  377: marmoset
-  378: white-headed capuchin
-  379: howler monkey
-  380: titi
-  381: Geoffroy's spider monkey
-  382: common squirrel monkey
-  383: ring-tailed lemur
-  384: indri
-  385: Asian elephant
-  386: African bush elephant
-  387: red panda
-  388: giant panda
-  389: snoek
-  390: eel
-  391: coho salmon
-  392: rock beauty
-  393: clownfish
-  394: sturgeon
-  395: garfish
-  396: lionfish
-  397: pufferfish
-  398: abacus
-  399: abaya
-  400: academic gown
-  401: accordion
-  402: acoustic guitar
-  403: aircraft carrier
-  404: airliner
-  405: airship
-  406: altar
-  407: ambulance
-  408: amphibious vehicle
-  409: analog clock
-  410: apiary
-  411: apron
-  412: waste container
-  413: assault rifle
-  414: backpack
-  415: bakery
-  416: balance beam
-  417: balloon
-  418: ballpoint pen
-  419: Band-Aid
-  420: banjo
-  421: baluster
-  422: barbell
-  423: barber chair
-  424: barbershop
-  425: barn
-  426: barometer
-  427: barrel
-  428: wheelbarrow
-  429: baseball
-  430: basketball
-  431: bassinet
-  432: bassoon
-  433: swimming cap
-  434: bath towel
-  435: bathtub
-  436: station wagon
-  437: lighthouse
-  438: beaker
-  439: military cap
-  440: beer bottle
-  441: beer glass
-  442: bell-cot
-  443: bib
-  444: tandem bicycle
-  445: bikini
-  446: ring binder
-  447: binoculars
-  448: birdhouse
-  449: boathouse
-  450: bobsleigh
-  451: bolo tie
-  452: poke bonnet
-  453: bookcase
-  454: bookstore
-  455: bottle cap
-  456: bow
-  457: bow tie
-  458: brass
-  459: bra
-  460: breakwater
-  461: breastplate
-  462: broom
-  463: bucket
-  464: buckle
-  465: bulletproof vest
-  466: high-speed train
-  467: butcher shop
-  468: taxicab
-  469: cauldron
-  470: candle
-  471: cannon
-  472: canoe
-  473: can opener
-  474: cardigan
-  475: car mirror
-  476: carousel
-  477: tool kit
-  478: carton
-  479: car wheel
-  480: automated teller machine
-  481: cassette
-  482: cassette player
-  483: castle
-  484: catamaran
-  485: CD player
-  486: cello
-  487: mobile phone
-  488: chain
-  489: chain-link fence
-  490: chain mail
-  491: chainsaw
-  492: chest
-  493: chiffonier
-  494: chime
-  495: china cabinet
-  496: Christmas stocking
-  497: church
-  498: movie theater
-  499: cleaver
-  500: cliff dwelling
-  501: cloak
-  502: clogs
-  503: cocktail shaker
-  504: coffee mug
-  505: coffeemaker
-  506: coil
-  507: combination lock
-  508: computer keyboard
-  509: confectionery store
-  510: container ship
-  511: convertible
-  512: corkscrew
-  513: cornet
-  514: cowboy boot
-  515: cowboy hat
-  516: cradle
-  517: crane (machine)
-  518: crash helmet
-  519: crate
-  520: infant bed
-  521: Crock Pot
-  522: croquet ball
-  523: crutch
-  524: cuirass
-  525: dam
-  526: desk
-  527: desktop computer
-  528: rotary dial telephone
-  529: diaper
-  530: digital clock
-  531: digital watch
-  532: dining table
-  533: dishcloth
-  534: dishwasher
-  535: disc brake
-  536: dock
-  537: dog sled
-  538: dome
-  539: doormat
-  540: drilling rig
-  541: drum
-  542: drumstick
-  543: dumbbell
-  544: Dutch oven
-  545: electric fan
-  546: electric guitar
-  547: electric locomotive
-  548: entertainment center
-  549: envelope
-  550: espresso machine
-  551: face powder
-  552: feather boa
-  553: filing cabinet
-  554: fireboat
-  555: fire engine
-  556: fire screen sheet
-  557: flagpole
-  558: flute
-  559: folding chair
-  560: football helmet
-  561: forklift
-  562: fountain
-  563: fountain pen
-  564: four-poster bed
-  565: freight car
-  566: French horn
-  567: frying pan
-  568: fur coat
-  569: garbage truck
-  570: gas mask
-  571: gas pump
-  572: goblet
-  573: go-kart
-  574: golf ball
-  575: golf cart
-  576: gondola
-  577: gong
-  578: gown
-  579: grand piano
-  580: greenhouse
-  581: grille
-  582: grocery store
-  583: guillotine
-  584: barrette
-  585: hair spray
-  586: half-track
-  587: hammer
-  588: hamper
-  589: hair dryer
-  590: hand-held computer
-  591: handkerchief
-  592: hard disk drive
-  593: harmonica
-  594: harp
-  595: harvester
-  596: hatchet
-  597: holster
-  598: home theater
-  599: honeycomb
-  600: hook
-  601: hoop skirt
-  602: horizontal bar
-  603: horse-drawn vehicle
-  604: hourglass
-  605: iPod
-  606: clothes iron
-  607: jack-o'-lantern
-  608: jeans
-  609: jeep
-  610: T-shirt
-  611: jigsaw puzzle
-  612: pulled rickshaw
-  613: joystick
-  614: kimono
-  615: knee pad
-  616: knot
-  617: lab coat
-  618: ladle
-  619: lampshade
-  620: laptop computer
-  621: lawn mower
-  622: lens cap
-  623: paper knife
-  624: library
-  625: lifeboat
-  626: lighter
-  627: limousine
-  628: ocean liner
-  629: lipstick
-  630: slip-on shoe
-  631: lotion
-  632: speaker
-  633: loupe
-  634: sawmill
-  635: magnetic compass
-  636: mail bag
-  637: mailbox
-  638: tights
-  639: tank suit
-  640: manhole cover
-  641: maraca
-  642: marimba
-  643: mask
-  644: match
-  645: maypole
-  646: maze
-  647: measuring cup
-  648: medicine chest
-  649: megalith
-  650: microphone
-  651: microwave oven
-  652: military uniform
-  653: milk can
-  654: minibus
-  655: miniskirt
-  656: minivan
-  657: missile
-  658: mitten
-  659: mixing bowl
-  660: mobile home
-  661: Model T
-  662: modem
-  663: monastery
-  664: monitor
-  665: moped
-  666: mortar
-  667: square academic cap
-  668: mosque
-  669: mosquito net
-  670: scooter
-  671: mountain bike
-  672: tent
-  673: computer mouse
-  674: mousetrap
-  675: moving van
-  676: muzzle
-  677: nail
-  678: neck brace
-  679: necklace
-  680: nipple
-  681: notebook computer
-  682: obelisk
-  683: oboe
-  684: ocarina
-  685: odometer
-  686: oil filter
-  687: organ
-  688: oscilloscope
-  689: overskirt
-  690: bullock cart
-  691: oxygen mask
-  692: packet
-  693: paddle
-  694: paddle wheel
-  695: padlock
-  696: paintbrush
-  697: pajamas
-  698: palace
-  699: pan flute
-  700: paper towel
-  701: parachute
-  702: parallel bars
-  703: park bench
-  704: parking meter
-  705: passenger car
-  706: patio
-  707: payphone
-  708: pedestal
-  709: pencil case
-  710: pencil sharpener
-  711: perfume
-  712: Petri dish
-  713: photocopier
-  714: plectrum
-  715: Pickelhaube
-  716: picket fence
-  717: pickup truck
-  718: pier
-  719: piggy bank
-  720: pill bottle
-  721: pillow
-  722: ping-pong ball
-  723: pinwheel
-  724: pirate ship
-  725: pitcher
-  726: hand plane
-  727: planetarium
-  728: plastic bag
-  729: plate rack
-  730: plow
-  731: plunger
-  732: Polaroid camera
-  733: pole
-  734: police van
-  735: poncho
-  736: billiard table
-  737: soda bottle
-  738: pot
-  739: potter's wheel
-  740: power drill
-  741: prayer rug
-  742: printer
-  743: prison
-  744: projectile
-  745: projector
-  746: hockey puck
-  747: punching bag
-  748: purse
-  749: quill
-  750: quilt
-  751: race car
-  752: racket
-  753: radiator
-  754: radio
-  755: radio telescope
-  756: rain barrel
-  757: recreational vehicle
-  758: reel
-  759: reflex camera
-  760: refrigerator
-  761: remote control
-  762: restaurant
-  763: revolver
-  764: rifle
-  765: rocking chair
-  766: rotisserie
-  767: eraser
-  768: rugby ball
-  769: ruler
-  770: running shoe
-  771: safe
-  772: safety pin
-  773: salt shaker
-  774: sandal
-  775: sarong
-  776: saxophone
-  777: scabbard
-  778: weighing scale
-  779: school bus
-  780: schooner
-  781: scoreboard
-  782: CRT screen
-  783: screw
-  784: screwdriver
-  785: seat belt
-  786: sewing machine
-  787: shield
-  788: shoe store
-  789: shoji
-  790: shopping basket
-  791: shopping cart
-  792: shovel
-  793: shower cap
-  794: shower curtain
-  795: ski
-  796: ski mask
-  797: sleeping bag
-  798: slide rule
-  799: sliding door
-  800: slot machine
-  801: snorkel
-  802: snowmobile
-  803: snowplow
-  804: soap dispenser
-  805: soccer ball
-  806: sock
-  807: solar thermal collector
-  808: sombrero
-  809: soup bowl
-  810: space bar
-  811: space heater
-  812: space shuttle
-  813: spatula
-  814: motorboat
-  815: spider web
-  816: spindle
-  817: sports car
-  818: spotlight
-  819: stage
-  820: steam locomotive
-  821: through arch bridge
-  822: steel drum
-  823: stethoscope
-  824: scarf
-  825: stone wall
-  826: stopwatch
-  827: stove
-  828: strainer
-  829: tram
-  830: stretcher
-  831: couch
-  832: stupa
-  833: submarine
-  834: suit
-  835: sundial
-  836: sunglass
-  837: sunglasses
-  838: sunscreen
-  839: suspension bridge
-  840: mop
-  841: sweatshirt
-  842: swimsuit
-  843: swing
-  844: switch
-  845: syringe
-  846: table lamp
-  847: tank
-  848: tape player
-  849: teapot
-  850: teddy bear
-  851: television
-  852: tennis ball
-  853: thatched roof
-  854: front curtain
-  855: thimble
-  856: threshing machine
-  857: throne
-  858: tile roof
-  859: toaster
-  860: tobacco shop
-  861: toilet seat
-  862: torch
-  863: totem pole
-  864: tow truck
-  865: toy store
-  866: tractor
-  867: semi-trailer truck
-  868: tray
-  869: trench coat
-  870: tricycle
-  871: trimaran
-  872: tripod
-  873: triumphal arch
-  874: trolleybus
-  875: trombone
-  876: tub
-  877: turnstile
-  878: typewriter keyboard
-  879: umbrella
-  880: unicycle
-  881: upright piano
-  882: vacuum cleaner
-  883: vase
-  884: vault
-  885: velvet
-  886: vending machine
-  887: vestment
-  888: viaduct
-  889: violin
-  890: volleyball
-  891: waffle iron
-  892: wall clock
-  893: wallet
-  894: wardrobe
-  895: military aircraft
-  896: sink
-  897: washing machine
-  898: water bottle
-  899: water jug
-  900: water tower
-  901: whiskey jug
-  902: whistle
-  903: wig
-  904: window screen
-  905: window shade
-  906: Windsor tie
-  907: wine bottle
-  908: wing
-  909: wok
-  910: wooden spoon
-  911: wool
-  912: split-rail fence
-  913: shipwreck
-  914: yawl
-  915: yurt
-  916: website
-  917: comic book
-  918: crossword
-  919: traffic sign
-  920: traffic light
-  921: dust jacket
-  922: menu
-  923: plate
-  924: guacamole
-  925: consomme
-  926: hot pot
-  927: trifle
-  928: ice cream
-  929: ice pop
-  930: baguette
-  931: bagel
-  932: pretzel
-  933: cheeseburger
-  934: hot dog
-  935: mashed potato
-  936: cabbage
-  937: broccoli
-  938: cauliflower
-  939: zucchini
-  940: spaghetti squash
-  941: acorn squash
-  942: butternut squash
-  943: cucumber
-  944: artichoke
-  945: bell pepper
-  946: cardoon
-  947: mushroom
-  948: Granny Smith
-  949: strawberry
-  950: orange
-  951: lemon
-  952: fig
-  953: pineapple
-  954: banana
-  955: jackfruit
-  956: custard apple
-  957: pomegranate
-  958: hay
-  959: carbonara
-  960: chocolate syrup
-  961: dough
-  962: meatloaf
-  963: pizza
-  964: pot pie
-  965: burrito
-  966: red wine
-  967: espresso
-  968: cup
-  969: eggnog
-  970: alp
-  971: bubble
-  972: cliff
-  973: coral reef
-  974: geyser
-  975: lakeshore
-  976: promontory
-  977: shoal
-  978: seashore
-  979: valley
-  980: volcano
-  981: baseball player
-  982: bridegroom
-  983: scuba diver
-  984: rapeseed
-  985: daisy
-  986: yellow lady's slipper
-  987: corn
-  988: acorn
-  989: rose hip
-  990: horse chestnut seed
-  991: coral fungus
-  992: agaric
-  993: gyromitra
-  994: stinkhorn mushroom
-  995: earth star
-  996: hen-of-the-woods
-  997: bolete
-  998: ear
-  999: toilet paper
-
-# Download script/URL (optional)
-download: data/scripts/get_imagenet.sh

yolov5/data/ImageNet10.yaml

@@ -1,31 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
-# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
-# Example usage: python classify/train.py --data imagenet
-# parent
-# ├── yolov5
-# └── datasets
-#     └── imagenet10  ← downloads here
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/imagenet10 # dataset root dir
-train: train # train images (relative to 'path') 1281167 images
-val: val # val images (relative to 'path') 50000 images
-test: # test images (optional)
-
-# Classes
-names:
-  0: tench
-  1: goldfish
-  2: great white shark
-  3: tiger shark
-  4: hammerhead shark
-  5: electric ray
-  6: stingray
-  7: cock
-  8: hen
-  9: ostrich
-
-# Download script/URL (optional)
-download: data/scripts/get_imagenet10.sh

yolov5/data/ImageNet100.yaml

@@ -1,120 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
-# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
-# Example usage: python classify/train.py --data imagenet
-# parent
-# ├── yolov5
-# └── datasets
-#     └── imagenet100  ← downloads here
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/imagenet100 # dataset root dir
-train: train # train images (relative to 'path') 1281167 images
-val: val # val images (relative to 'path') 50000 images
-test: # test images (optional)
-
-# Classes
-names:
-  0: tench
-  1: goldfish
-  2: great white shark
-  3: tiger shark
-  4: hammerhead shark
-  5: electric ray
-  6: stingray
-  7: cock
-  8: hen
-  9: ostrich
-  10: brambling
-  11: goldfinch
-  12: house finch
-  13: junco
-  14: indigo bunting
-  15: American robin
-  16: bulbul
-  17: jay
-  18: magpie
-  19: chickadee
-  20: American dipper
-  21: kite
-  22: bald eagle
-  23: vulture
-  24: great grey owl
-  25: fire salamander
-  26: smooth newt
-  27: newt
-  28: spotted salamander
-  29: axolotl
-  30: American bullfrog
-  31: tree frog
-  32: tailed frog
-  33: loggerhead sea turtle
-  34: leatherback sea turtle
-  35: mud turtle
-  36: terrapin
-  37: box turtle
-  38: banded gecko
-  39: green iguana
-  40: Carolina anole
-  41: desert grassland whiptail lizard
-  42: agama
-  43: frilled-necked lizard
-  44: alligator lizard
-  45: Gila monster
-  46: European green lizard
-  47: chameleon
-  48: Komodo dragon
-  49: Nile crocodile
-  50: American alligator
-  51: triceratops
-  52: worm snake
-  53: ring-necked snake
-  54: eastern hog-nosed snake
-  55: smooth green snake
-  56: kingsnake
-  57: garter snake
-  58: water snake
-  59: vine snake
-  60: night snake
-  61: boa constrictor
-  62: African rock python
-  63: Indian cobra
-  64: green mamba
-  65: sea snake
-  66: Saharan horned viper
-  67: eastern diamondback rattlesnake
-  68: sidewinder
-  69: trilobite
-  70: harvestman
-  71: scorpion
-  72: yellow garden spider
-  73: barn spider
-  74: European garden spider
-  75: southern black widow
-  76: tarantula
-  77: wolf spider
-  78: tick
-  79: centipede
-  80: black grouse
-  81: ptarmigan
-  82: ruffed grouse
-  83: prairie grouse
-  84: peacock
-  85: quail
-  86: partridge
-  87: grey parrot
-  88: macaw
-  89: sulphur-crested cockatoo
-  90: lorikeet
-  91: coucal
-  92: bee eater
-  93: hornbill
-  94: hummingbird
-  95: jacamar
-  96: toucan
-  97: duck
-  98: red-breasted merganser
-  99: goose
-# Download script/URL (optional)
-download: data/scripts/get_imagenet100.sh

yolov5/data/ImageNet1000.yaml

@@ -1,1021 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
-# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
-# Example usage: python classify/train.py --data imagenet
-# parent
-# ├── yolov5
-# └── datasets
-#     └── imagenet100  ← downloads here
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/imagenet1000 # dataset root dir
-train: train # train images (relative to 'path') 1281167 images
-val: val # val images (relative to 'path') 50000 images
-test: # test images (optional)
-
-# Classes
-names:
-  0: tench
-  1: goldfish
-  2: great white shark
-  3: tiger shark
-  4: hammerhead shark
-  5: electric ray
-  6: stingray
-  7: cock
-  8: hen
-  9: ostrich
-  10: brambling
-  11: goldfinch
-  12: house finch
-  13: junco
-  14: indigo bunting
-  15: American robin
-  16: bulbul
-  17: jay
-  18: magpie
-  19: chickadee
-  20: American dipper
-  21: kite
-  22: bald eagle
-  23: vulture
-  24: great grey owl
-  25: fire salamander
-  26: smooth newt
-  27: newt
-  28: spotted salamander
-  29: axolotl
-  30: American bullfrog
-  31: tree frog
-  32: tailed frog
-  33: loggerhead sea turtle
-  34: leatherback sea turtle
-  35: mud turtle
-  36: terrapin
-  37: box turtle
-  38: banded gecko
-  39: green iguana
-  40: Carolina anole
-  41: desert grassland whiptail lizard
-  42: agama
-  43: frilled-necked lizard
-  44: alligator lizard
-  45: Gila monster
-  46: European green lizard
-  47: chameleon
-  48: Komodo dragon
-  49: Nile crocodile
-  50: American alligator
-  51: triceratops
-  52: worm snake
-  53: ring-necked snake
-  54: eastern hog-nosed snake
-  55: smooth green snake
-  56: kingsnake
-  57: garter snake
-  58: water snake
-  59: vine snake
-  60: night snake
-  61: boa constrictor
-  62: African rock python
-  63: Indian cobra
-  64: green mamba
-  65: sea snake
-  66: Saharan horned viper
-  67: eastern diamondback rattlesnake
-  68: sidewinder
-  69: trilobite
-  70: harvestman
-  71: scorpion
-  72: yellow garden spider
-  73: barn spider
-  74: European garden spider
-  75: southern black widow
-  76: tarantula
-  77: wolf spider
-  78: tick
-  79: centipede
-  80: black grouse
-  81: ptarmigan
-  82: ruffed grouse
-  83: prairie grouse
-  84: peacock
-  85: quail
-  86: partridge
-  87: grey parrot
-  88: macaw
-  89: sulphur-crested cockatoo
-  90: lorikeet
-  91: coucal
-  92: bee eater
-  93: hornbill
-  94: hummingbird
-  95: jacamar
-  96: toucan
-  97: duck
-  98: red-breasted merganser
-  99: goose
-  100: black swan
-  101: tusker
-  102: echidna
-  103: platypus
-  104: wallaby
-  105: koala
-  106: wombat
-  107: jellyfish
-  108: sea anemone
-  109: brain coral
-  110: flatworm
-  111: nematode
-  112: conch
-  113: snail
-  114: slug
-  115: sea slug
-  116: chiton
-  117: chambered nautilus
-  118: Dungeness crab
-  119: rock crab
-  120: fiddler crab
-  121: red king crab
-  122: American lobster
-  123: spiny lobster
-  124: crayfish
-  125: hermit crab
-  126: isopod
-  127: white stork
-  128: black stork
-  129: spoonbill
-  130: flamingo
-  131: little blue heron
-  132: great egret
-  133: bittern
-  134: crane (bird)
-  135: limpkin
-  136: common gallinule
-  137: American coot
-  138: bustard
-  139: ruddy turnstone
-  140: dunlin
-  141: common redshank
-  142: dowitcher
-  143: oystercatcher
-  144: pelican
-  145: king penguin
-  146: albatross
-  147: grey whale
-  148: killer whale
-  149: dugong
-  150: sea lion
-  151: Chihuahua
-  152: Japanese Chin
-  153: Maltese
-  154: Pekingese
-  155: Shih Tzu
-  156: King Charles Spaniel
-  157: Papillon
-  158: toy terrier
-  159: Rhodesian Ridgeback
-  160: Afghan Hound
-  161: Basset Hound
-  162: Beagle
-  163: Bloodhound
-  164: Bluetick Coonhound
-  165: Black and Tan Coonhound
-  166: Treeing Walker Coonhound
-  167: English foxhound
-  168: Redbone Coonhound
-  169: borzoi
-  170: Irish Wolfhound
-  171: Italian Greyhound
-  172: Whippet
-  173: Ibizan Hound
-  174: Norwegian Elkhound
-  175: Otterhound
-  176: Saluki
-  177: Scottish Deerhound
-  178: Weimaraner
-  179: Staffordshire Bull Terrier
-  180: American Staffordshire Terrier
-  181: Bedlington Terrier
-  182: Border Terrier
-  183: Kerry Blue Terrier
-  184: Irish Terrier
-  185: Norfolk Terrier
-  186: Norwich Terrier
-  187: Yorkshire Terrier
-  188: Wire Fox Terrier
-  189: Lakeland Terrier
-  190: Sealyham Terrier
-  191: Airedale Terrier
-  192: Cairn Terrier
-  193: Australian Terrier
-  194: Dandie Dinmont Terrier
-  195: Boston Terrier
-  196: Miniature Schnauzer
-  197: Giant Schnauzer
-  198: Standard Schnauzer
-  199: Scottish Terrier
-  200: Tibetan Terrier
-  201: Australian Silky Terrier
-  202: Soft-coated Wheaten Terrier
-  203: West Highland White Terrier
-  204: Lhasa Apso
-  205: Flat-Coated Retriever
-  206: Curly-coated Retriever
-  207: Golden Retriever
-  208: Labrador Retriever
-  209: Chesapeake Bay Retriever
-  210: German Shorthaired Pointer
-  211: Vizsla
-  212: English Setter
-  213: Irish Setter
-  214: Gordon Setter
-  215: Brittany
-  216: Clumber Spaniel
-  217: English Springer Spaniel
-  218: Welsh Springer Spaniel
-  219: Cocker Spaniels
-  220: Sussex Spaniel
-  221: Irish Water Spaniel
-  222: Kuvasz
-  223: Schipperke
-  224: Groenendael
-  225: Malinois
-  226: Briard
-  227: Australian Kelpie
-  228: Komondor
-  229: Old English Sheepdog
-  230: Shetland Sheepdog
-  231: collie
-  232: Border Collie
-  233: Bouvier des Flandres
-  234: Rottweiler
-  235: German Shepherd Dog
-  236: Dobermann
-  237: Miniature Pinscher
-  238: Greater Swiss Mountain Dog
-  239: Bernese Mountain Dog
-  240: Appenzeller Sennenhund
-  241: Entlebucher Sennenhund
-  242: Boxer
-  243: Bullmastiff
-  244: Tibetan Mastiff
-  245: French Bulldog
-  246: Great Dane
-  247: St. Bernard
-  248: husky
-  249: Alaskan Malamute
-  250: Siberian Husky
-  251: Dalmatian
-  252: Affenpinscher
-  253: Basenji
-  254: pug
-  255: Leonberger
-  256: Newfoundland
-  257: Pyrenean Mountain Dog
-  258: Samoyed
-  259: Pomeranian
-  260: Chow Chow
-  261: Keeshond
-  262: Griffon Bruxellois
-  263: Pembroke Welsh Corgi
-  264: Cardigan Welsh Corgi
-  265: Toy Poodle
-  266: Miniature Poodle
-  267: Standard Poodle
-  268: Mexican hairless dog
-  269: grey wolf
-  270: Alaskan tundra wolf
-  271: red wolf
-  272: coyote
-  273: dingo
-  274: dhole
-  275: African wild dog
-  276: hyena
-  277: red fox
-  278: kit fox
-  279: Arctic fox
-  280: grey fox
-  281: tabby cat
-  282: tiger cat
-  283: Persian cat
-  284: Siamese cat
-  285: Egyptian Mau
-  286: cougar
-  287: lynx
-  288: leopard
-  289: snow leopard
-  290: jaguar
-  291: lion
-  292: tiger
-  293: cheetah
-  294: brown bear
-  295: American black bear
-  296: polar bear
-  297: sloth bear
-  298: mongoose
-  299: meerkat
-  300: tiger beetle
-  301: ladybug
-  302: ground beetle
-  303: longhorn beetle
-  304: leaf beetle
-  305: dung beetle
-  306: rhinoceros beetle
-  307: weevil
-  308: fly
-  309: bee
-  310: ant
-  311: grasshopper
-  312: cricket
-  313: stick insect
-  314: cockroach
-  315: mantis
-  316: cicada
-  317: leafhopper
-  318: lacewing
-  319: dragonfly
-  320: damselfly
-  321: red admiral
-  322: ringlet
-  323: monarch butterfly
-  324: small white
-  325: sulphur butterfly
-  326: gossamer-winged butterfly
-  327: starfish
-  328: sea urchin
-  329: sea cucumber
-  330: cottontail rabbit
-  331: hare
-  332: Angora rabbit
-  333: hamster
-  334: porcupine
-  335: fox squirrel
-  336: marmot
-  337: beaver
-  338: guinea pig
-  339: common sorrel
-  340: zebra
-  341: pig
-  342: wild boar
-  343: warthog
-  344: hippopotamus
-  345: ox
-  346: water buffalo
-  347: bison
-  348: ram
-  349: bighorn sheep
-  350: Alpine ibex
-  351: hartebeest
-  352: impala
-  353: gazelle
-  354: dromedary
-  355: llama
-  356: weasel
-  357: mink
-  358: European polecat
-  359: black-footed ferret
-  360: otter
-  361: skunk
-  362: badger
-  363: armadillo
-  364: three-toed sloth
-  365: orangutan
-  366: gorilla
-  367: chimpanzee
-  368: gibbon
-  369: siamang
-  370: guenon
-  371: patas monkey
-  372: baboon
-  373: macaque
-  374: langur
-  375: black-and-white colobus
-  376: proboscis monkey
-  377: marmoset
-  378: white-headed capuchin
-  379: howler monkey
-  380: titi
-  381: Geoffroy's spider monkey
-  382: common squirrel monkey
-  383: ring-tailed lemur
-  384: indri
-  385: Asian elephant
-  386: African bush elephant
-  387: red panda
-  388: giant panda
-  389: snoek
-  390: eel
-  391: coho salmon
-  392: rock beauty
-  393: clownfish
-  394: sturgeon
-  395: garfish
-  396: lionfish
-  397: pufferfish
-  398: abacus
-  399: abaya
-  400: academic gown
-  401: accordion
-  402: acoustic guitar
-  403: aircraft carrier
-  404: airliner
-  405: airship
-  406: altar
-  407: ambulance
-  408: amphibious vehicle
-  409: analog clock
-  410: apiary
-  411: apron
-  412: waste container
-  413: assault rifle
-  414: backpack
-  415: bakery
-  416: balance beam
-  417: balloon
-  418: ballpoint pen
-  419: Band-Aid
-  420: banjo
-  421: baluster
-  422: barbell
-  423: barber chair
-  424: barbershop
-  425: barn
-  426: barometer
-  427: barrel
-  428: wheelbarrow
-  429: baseball
-  430: basketball
-  431: bassinet
-  432: bassoon
-  433: swimming cap
-  434: bath towel
-  435: bathtub
-  436: station wagon
-  437: lighthouse
-  438: beaker
-  439: military cap
-  440: beer bottle
-  441: beer glass
-  442: bell-cot
-  443: bib
-  444: tandem bicycle
-  445: bikini
-  446: ring binder
-  447: binoculars
-  448: birdhouse
-  449: boathouse
-  450: bobsleigh
-  451: bolo tie
-  452: poke bonnet
-  453: bookcase
-  454: bookstore
-  455: bottle cap
-  456: bow
-  457: bow tie
-  458: brass
-  459: bra
-  460: breakwater
-  461: breastplate
-  462: broom
-  463: bucket
-  464: buckle
-  465: bulletproof vest
-  466: high-speed train
-  467: butcher shop
-  468: taxicab
-  469: cauldron
-  470: candle
-  471: cannon
-  472: canoe
-  473: can opener
-  474: cardigan
-  475: car mirror
-  476: carousel
-  477: tool kit
-  478: carton
-  479: car wheel
-  480: automated teller machine
-  481: cassette
-  482: cassette player
-  483: castle
-  484: catamaran
-  485: CD player
-  486: cello
-  487: mobile phone
-  488: chain
-  489: chain-link fence
-  490: chain mail
-  491: chainsaw
-  492: chest
-  493: chiffonier
-  494: chime
-  495: china cabinet
-  496: Christmas stocking
-  497: church
-  498: movie theater
-  499: cleaver
-  500: cliff dwelling
-  501: cloak
-  502: clogs
-  503: cocktail shaker
-  504: coffee mug
-  505: coffeemaker
-  506: coil
-  507: combination lock
-  508: computer keyboard
-  509: confectionery store
-  510: container ship
-  511: convertible
-  512: corkscrew
-  513: cornet
-  514: cowboy boot
-  515: cowboy hat
-  516: cradle
-  517: crane (machine)
-  518: crash helmet
-  519: crate
-  520: infant bed
-  521: Crock Pot
-  522: croquet ball
-  523: crutch
-  524: cuirass
-  525: dam
-  526: desk
-  527: desktop computer
-  528: rotary dial telephone
-  529: diaper
-  530: digital clock
-  531: digital watch
-  532: dining table
-  533: dishcloth
-  534: dishwasher
-  535: disc brake
-  536: dock
-  537: dog sled
-  538: dome
-  539: doormat
-  540: drilling rig
-  541: drum
-  542: drumstick
-  543: dumbbell
-  544: Dutch oven
-  545: electric fan
-  546: electric guitar
-  547: electric locomotive
-  548: entertainment center
-  549: envelope
-  550: espresso machine
-  551: face powder
-  552: feather boa
-  553: filing cabinet
-  554: fireboat
-  555: fire engine
-  556: fire screen sheet
-  557: flagpole
-  558: flute
-  559: folding chair
-  560: football helmet
-  561: forklift
-  562: fountain
-  563: fountain pen
-  564: four-poster bed
-  565: freight car
-  566: French horn
-  567: frying pan
-  568: fur coat
-  569: garbage truck
-  570: gas mask
-  571: gas pump
-  572: goblet
-  573: go-kart
-  574: golf ball
-  575: golf cart
-  576: gondola
-  577: gong
-  578: gown
-  579: grand piano
-  580: greenhouse
-  581: grille
-  582: grocery store
-  583: guillotine
-  584: barrette
-  585: hair spray
-  586: half-track
-  587: hammer
-  588: hamper
-  589: hair dryer
-  590: hand-held computer
-  591: handkerchief
-  592: hard disk drive
-  593: harmonica
-  594: harp
-  595: harvester
-  596: hatchet
-  597: holster
-  598: home theater
-  599: honeycomb
-  600: hook
-  601: hoop skirt
-  602: horizontal bar
-  603: horse-drawn vehicle
-  604: hourglass
-  605: iPod
-  606: clothes iron
-  607: jack-o'-lantern
-  608: jeans
-  609: jeep
-  610: T-shirt
-  611: jigsaw puzzle
-  612: pulled rickshaw
-  613: joystick
-  614: kimono
-  615: knee pad
-  616: knot
-  617: lab coat
-  618: ladle
-  619: lampshade
-  620: laptop computer
-  621: lawn mower
-  622: lens cap
-  623: paper knife
-  624: library
-  625: lifeboat
-  626: lighter
-  627: limousine
-  628: ocean liner
-  629: lipstick
-  630: slip-on shoe
-  631: lotion
-  632: speaker
-  633: loupe
-  634: sawmill
-  635: magnetic compass
-  636: mail bag
-  637: mailbox
-  638: tights
-  639: tank suit
-  640: manhole cover
-  641: maraca
-  642: marimba
-  643: mask
-  644: match
-  645: maypole
-  646: maze
-  647: measuring cup
-  648: medicine chest
-  649: megalith
-  650: microphone
-  651: microwave oven
-  652: military uniform
-  653: milk can
-  654: minibus
-  655: miniskirt
-  656: minivan
-  657: missile
-  658: mitten
-  659: mixing bowl
-  660: mobile home
-  661: Model T
-  662: modem
-  663: monastery
-  664: monitor
-  665: moped
-  666: mortar
-  667: square academic cap
-  668: mosque
-  669: mosquito net
-  670: scooter
-  671: mountain bike
-  672: tent
-  673: computer mouse
-  674: mousetrap
-  675: moving van
-  676: muzzle
-  677: nail
-  678: neck brace
-  679: necklace
-  680: nipple
-  681: notebook computer
-  682: obelisk
-  683: oboe
-  684: ocarina
-  685: odometer
-  686: oil filter
-  687: organ
-  688: oscilloscope
-  689: overskirt
-  690: bullock cart
-  691: oxygen mask
-  692: packet
-  693: paddle
-  694: paddle wheel
-  695: padlock
-  696: paintbrush
-  697: pajamas
-  698: palace
-  699: pan flute
-  700: paper towel
-  701: parachute
-  702: parallel bars
-  703: park bench
-  704: parking meter
-  705: passenger car
-  706: patio
-  707: payphone
-  708: pedestal
-  709: pencil case
-  710: pencil sharpener
-  711: perfume
-  712: Petri dish
-  713: photocopier
-  714: plectrum
-  715: Pickelhaube
-  716: picket fence
-  717: pickup truck
-  718: pier
-  719: piggy bank
-  720: pill bottle
-  721: pillow
-  722: ping-pong ball
-  723: pinwheel
-  724: pirate ship
-  725: pitcher
-  726: hand plane
-  727: planetarium
-  728: plastic bag
-  729: plate rack
-  730: plow
-  731: plunger
-  732: Polaroid camera
-  733: pole
-  734: police van
-  735: poncho
-  736: billiard table
-  737: soda bottle
-  738: pot
-  739: potter's wheel
-  740: power drill
-  741: prayer rug
-  742: printer
-  743: prison
-  744: projectile
-  745: projector
-  746: hockey puck
-  747: punching bag
-  748: purse
-  749: quill
-  750: quilt
-  751: race car
-  752: racket
-  753: radiator
-  754: radio
-  755: radio telescope
-  756: rain barrel
-  757: recreational vehicle
-  758: reel
-  759: reflex camera
-  760: refrigerator
-  761: remote control
-  762: restaurant
-  763: revolver
-  764: rifle
-  765: rocking chair
-  766: rotisserie
-  767: eraser
-  768: rugby ball
-  769: ruler
-  770: running shoe
-  771: safe
-  772: safety pin
-  773: salt shaker
-  774: sandal
-  775: sarong
-  776: saxophone
-  777: scabbard
-  778: weighing scale
-  779: school bus
-  780: schooner
-  781: scoreboard
-  782: CRT screen
-  783: screw
-  784: screwdriver
-  785: seat belt
-  786: sewing machine
-  787: shield
-  788: shoe store
-  789: shoji
-  790: shopping basket
-  791: shopping cart
-  792: shovel
-  793: shower cap
-  794: shower curtain
-  795: ski
-  796: ski mask
-  797: sleeping bag
-  798: slide rule
-  799: sliding door
-  800: slot machine
-  801: snorkel
-  802: snowmobile
-  803: snowplow
-  804: soap dispenser
-  805: soccer ball
-  806: sock
-  807: solar thermal collector
-  808: sombrero
-  809: soup bowl
-  810: space bar
-  811: space heater
-  812: space shuttle
-  813: spatula
-  814: motorboat
-  815: spider web
-  816: spindle
-  817: sports car
-  818: spotlight
-  819: stage
-  820: steam locomotive
-  821: through arch bridge
-  822: steel drum
-  823: stethoscope
-  824: scarf
-  825: stone wall
-  826: stopwatch
-  827: stove
-  828: strainer
-  829: tram
-  830: stretcher
-  831: couch
-  832: stupa
-  833: submarine
-  834: suit
-  835: sundial
-  836: sunglass
-  837: sunglasses
-  838: sunscreen
-  839: suspension bridge
-  840: mop
-  841: sweatshirt
-  842: swimsuit
-  843: swing
-  844: switch
-  845: syringe
-  846: table lamp
-  847: tank
-  848: tape player
-  849: teapot
-  850: teddy bear
-  851: television
-  852: tennis ball
-  853: thatched roof
-  854: front curtain
-  855: thimble
-  856: threshing machine
-  857: throne
-  858: tile roof
-  859: toaster
-  860: tobacco shop
-  861: toilet seat
-  862: torch
-  863: totem pole
-  864: tow truck
-  865: toy store
-  866: tractor
-  867: semi-trailer truck
-  868: tray
-  869: trench coat
-  870: tricycle
-  871: trimaran
-  872: tripod
-  873: triumphal arch
-  874: trolleybus
-  875: trombone
-  876: tub
-  877: turnstile
-  878: typewriter keyboard
-  879: umbrella
-  880: unicycle
-  881: upright piano
-  882: vacuum cleaner
-  883: vase
-  884: vault
-  885: velvet
-  886: vending machine
-  887: vestment
-  888: viaduct
-  889: violin
-  890: volleyball
-  891: waffle iron
-  892: wall clock
-  893: wallet
-  894: wardrobe
-  895: military aircraft
-  896: sink
-  897: washing machine
-  898: water bottle
-  899: water jug
-  900: water tower
-  901: whiskey jug
-  902: whistle
-  903: wig
-  904: window screen
-  905: window shade
-  906: Windsor tie
-  907: wine bottle
-  908: wing
-  909: wok
-  910: wooden spoon
-  911: wool
-  912: split-rail fence
-  913: shipwreck
-  914: yawl
-  915: yurt
-  916: website
-  917: comic book
-  918: crossword
-  919: traffic sign
-  920: traffic light
-  921: dust jacket
-  922: menu
-  923: plate
-  924: guacamole
-  925: consomme
-  926: hot pot
-  927: trifle
-  928: ice cream
-  929: ice pop
-  930: baguette
-  931: bagel
-  932: pretzel
-  933: cheeseburger
-  934: hot dog
-  935: mashed potato
-  936: cabbage
-  937: broccoli
-  938: cauliflower
-  939: zucchini
-  940: spaghetti squash
-  941: acorn squash
-  942: butternut squash
-  943: cucumber
-  944: artichoke
-  945: bell pepper
-  946: cardoon
-  947: mushroom
-  948: Granny Smith
-  949: strawberry
-  950: orange
-  951: lemon
-  952: fig
-  953: pineapple
-  954: banana
-  955: jackfruit
-  956: custard apple
-  957: pomegranate
-  958: hay
-  959: carbonara
-  960: chocolate syrup
-  961: dough
-  962: meatloaf
-  963: pizza
-  964: pot pie
-  965: burrito
-  966: red wine
-  967: espresso
-  968: cup
-  969: eggnog
-  970: alp
-  971: bubble
-  972: cliff
-  973: coral reef
-  974: geyser
-  975: lakeshore
-  976: promontory
-  977: shoal
-  978: seashore
-  979: valley
-  980: volcano
-  981: baseball player
-  982: bridegroom
-  983: scuba diver
-  984: rapeseed
-  985: daisy
-  986: yellow lady's slipper
-  987: corn
-  988: acorn
-  989: rose hip
-  990: horse chestnut seed
-  991: coral fungus
-  992: agaric
-  993: gyromitra
-  994: stinkhorn mushroom
-  995: earth star
-  996: hen-of-the-woods
-  997: bolete
-  998: ear
-  999: toilet paper
-
-# Download script/URL (optional)
-download: data/scripts/get_imagenet1000.sh

yolov5/data/Objects365.yaml

@@ -1,437 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Objects365 dataset https://www.objects365.org/ by Megvii
-# Example usage: python train.py --data Objects365.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Objects365 # dataset root dir
-train: images/train # train images (relative to 'path') 1742289 images
-val: images/val # val images (relative to 'path') 80000 images
-test: # test images (optional)
-
-# Classes
-names:
-  0: Person
-  1: Sneakers
-  2: Chair
-  3: Other Shoes
-  4: Hat
-  5: Car
-  6: Lamp
-  7: Glasses
-  8: Bottle
-  9: Desk
-  10: Cup
-  11: Street Lights
-  12: Cabinet/shelf
-  13: Handbag/Satchel
-  14: Bracelet
-  15: Plate
-  16: Picture/Frame
-  17: Helmet
-  18: Book
-  19: Gloves
-  20: Storage box
-  21: Boat
-  22: Leather Shoes
-  23: Flower
-  24: Bench
-  25: Potted Plant
-  26: Bowl/Basin
-  27: Flag
-  28: Pillow
-  29: Boots
-  30: Vase
-  31: Microphone
-  32: Necklace
-  33: Ring
-  34: SUV
-  35: Wine Glass
-  36: Belt
-  37: Monitor/TV
-  38: Backpack
-  39: Umbrella
-  40: Traffic Light
-  41: Speaker
-  42: Watch
-  43: Tie
-  44: Trash bin Can
-  45: Slippers
-  46: Bicycle
-  47: Stool
-  48: Barrel/bucket
-  49: Van
-  50: Couch
-  51: Sandals
-  52: Basket
-  53: Drum
-  54: Pen/Pencil
-  55: Bus
-  56: Wild Bird
-  57: High Heels
-  58: Motorcycle
-  59: Guitar
-  60: Carpet
-  61: Cell Phone
-  62: Bread
-  63: Camera
-  64: Canned
-  65: Truck
-  66: Traffic cone
-  67: Cymbal
-  68: Lifesaver
-  69: Towel
-  70: Stuffed Toy
-  71: Candle
-  72: Sailboat
-  73: Laptop
-  74: Awning
-  75: Bed
-  76: Faucet
-  77: Tent
-  78: Horse
-  79: Mirror
-  80: Power outlet
-  81: Sink
-  82: Apple
-  83: Air Conditioner
-  84: Knife
-  85: Hockey Stick
-  86: Paddle
-  87: Pickup Truck
-  88: Fork
-  89: Traffic Sign
-  90: Balloon
-  91: Tripod
-  92: Dog
-  93: Spoon
-  94: Clock
-  95: Pot
-  96: Cow
-  97: Cake
-  98: Dinning Table
-  99: Sheep
-  100: Hanger
-  101: Blackboard/Whiteboard
-  102: Napkin
-  103: Other Fish
-  104: Orange/Tangerine
-  105: Toiletry
-  106: Keyboard
-  107: Tomato
-  108: Lantern
-  109: Machinery Vehicle
-  110: Fan
-  111: Green Vegetables
-  112: Banana
-  113: Baseball Glove
-  114: Airplane
-  115: Mouse
-  116: Train
-  117: Pumpkin
-  118: Soccer
-  119: Skiboard
-  120: Luggage
-  121: Nightstand
-  122: Tea pot
-  123: Telephone
-  124: Trolley
-  125: Head Phone
-  126: Sports Car
-  127: Stop Sign
-  128: Dessert
-  129: Scooter
-  130: Stroller
-  131: Crane
-  132: Remote
-  133: Refrigerator
-  134: Oven
-  135: Lemon
-  136: Duck
-  137: Baseball Bat
-  138: Surveillance Camera
-  139: Cat
-  140: Jug
-  141: Broccoli
-  142: Piano
-  143: Pizza
-  144: Elephant
-  145: Skateboard
-  146: Surfboard
-  147: Gun
-  148: Skating and Skiing shoes
-  149: Gas stove
-  150: Donut
-  151: Bow Tie
-  152: Carrot
-  153: Toilet
-  154: Kite
-  155: Strawberry
-  156: Other Balls
-  157: Shovel
-  158: Pepper
-  159: Computer Box
-  160: Toilet Paper
-  161: Cleaning Products
-  162: Chopsticks
-  163: Microwave
-  164: Pigeon
-  165: Baseball
-  166: Cutting/chopping Board
-  167: Coffee Table
-  168: Side Table
-  169: Scissors
-  170: Marker
-  171: Pie
-  172: Ladder
-  173: Snowboard
-  174: Cookies
-  175: Radiator
-  176: Fire Hydrant
-  177: Basketball
-  178: Zebra
-  179: Grape
-  180: Giraffe
-  181: Potato
-  182: Sausage
-  183: Tricycle
-  184: Violin
-  185: Egg
-  186: Fire Extinguisher
-  187: Candy
-  188: Fire Truck
-  189: Billiards
-  190: Converter
-  191: Bathtub
-  192: Wheelchair
-  193: Golf Club
-  194: Briefcase
-  195: Cucumber
-  196: Cigar/Cigarette
-  197: Paint Brush
-  198: Pear
-  199: Heavy Truck
-  200: Hamburger
-  201: Extractor
-  202: Extension Cord
-  203: Tong
-  204: Tennis Racket
-  205: Folder
-  206: American Football
-  207: earphone
-  208: Mask
-  209: Kettle
-  210: Tennis
-  211: Ship
-  212: Swing
-  213: Coffee Machine
-  214: Slide
-  215: Carriage
-  216: Onion
-  217: Green beans
-  218: Projector
-  219: Frisbee
-  220: Washing Machine/Drying Machine
-  221: Chicken
-  222: Printer
-  223: Watermelon
-  224: Saxophone
-  225: Tissue
-  226: Toothbrush
-  227: Ice cream
-  228: Hot-air balloon
-  229: Cello
-  230: French Fries
-  231: Scale
-  232: Trophy
-  233: Cabbage
-  234: Hot dog
-  235: Blender
-  236: Peach
-  237: Rice
-  238: Wallet/Purse
-  239: Volleyball
-  240: Deer
-  241: Goose
-  242: Tape
-  243: Tablet
-  244: Cosmetics
-  245: Trumpet
-  246: Pineapple
-  247: Golf Ball
-  248: Ambulance
-  249: Parking meter
-  250: Mango
-  251: Key
-  252: Hurdle
-  253: Fishing Rod
-  254: Medal
-  255: Flute
-  256: Brush
-  257: Penguin
-  258: Megaphone
-  259: Corn
-  260: Lettuce
-  261: Garlic
-  262: Swan
-  263: Helicopter
-  264: Green Onion
-  265: Sandwich
-  266: Nuts
-  267: Speed Limit Sign
-  268: Induction Cooker
-  269: Broom
-  270: Trombone
-  271: Plum
-  272: Rickshaw
-  273: Goldfish
-  274: Kiwi fruit
-  275: Router/modem
-  276: Poker Card
-  277: Toaster
-  278: Shrimp
-  279: Sushi
-  280: Cheese
-  281: Notepaper
-  282: Cherry
-  283: Pliers
-  284: CD
-  285: Pasta
-  286: Hammer
-  287: Cue
-  288: Avocado
-  289: Hamimelon
-  290: Flask
-  291: Mushroom
-  292: Screwdriver
-  293: Soap
-  294: Recorder
-  295: Bear
-  296: Eggplant
-  297: Board Eraser
-  298: Coconut
-  299: Tape Measure/Ruler
-  300: Pig
-  301: Showerhead
-  302: Globe
-  303: Chips
-  304: Steak
-  305: Crosswalk Sign
-  306: Stapler
-  307: Camel
-  308: Formula 1
-  309: Pomegranate
-  310: Dishwasher
-  311: Crab
-  312: Hoverboard
-  313: Meat ball
-  314: Rice Cooker
-  315: Tuba
-  316: Calculator
-  317: Papaya
-  318: Antelope
-  319: Parrot
-  320: Seal
-  321: Butterfly
-  322: Dumbbell
-  323: Donkey
-  324: Lion
-  325: Urinal
-  326: Dolphin
-  327: Electric Drill
-  328: Hair Dryer
-  329: Egg tart
-  330: Jellyfish
-  331: Treadmill
-  332: Lighter
-  333: Grapefruit
-  334: Game board
-  335: Mop
-  336: Radish
-  337: Baozi
-  338: Target
-  339: French
-  340: Spring Rolls
-  341: Monkey
-  342: Rabbit
-  343: Pencil Case
-  344: Yak
-  345: Red Cabbage
-  346: Binoculars
-  347: Asparagus
-  348: Barbell
-  349: Scallop
-  350: Noddles
-  351: Comb
-  352: Dumpling
-  353: Oyster
-  354: Table Tennis paddle
-  355: Cosmetics Brush/Eyeliner Pencil
-  356: Chainsaw
-  357: Eraser
-  358: Lobster
-  359: Durian
-  360: Okra
-  361: Lipstick
-  362: Cosmetics Mirror
-  363: Curling
-  364: Table Tennis
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  from tqdm import tqdm
-
-  from utils.general import Path, check_requirements, download, np, xyxy2xywhn
-
-  check_requirements('pycocotools>=2.0')
-  from pycocotools.coco import COCO
-
-  # Make Directories
-  dir = Path(yaml['path'])  # dataset root dir
-  for p in 'images', 'labels':
-      (dir / p).mkdir(parents=True, exist_ok=True)
-      for q in 'train', 'val':
-          (dir / p / q).mkdir(parents=True, exist_ok=True)
-
-  # Train, Val Splits
-  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
-      print(f"Processing {split} in {patches} patches ...")
-      images, labels = dir / 'images' / split, dir / 'labels' / split
-
-      # Download
-      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
-      if split == 'train':
-          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
-          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
-      elif split == 'val':
-          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
-          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
-          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
-
-      # Move
-      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
-          f.rename(images / f.name)  # move to /images/{split}
-
-      # Labels
-      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
-      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
-      for cid, cat in enumerate(names):
-          catIds = coco.getCatIds(catNms=[cat])
-          imgIds = coco.getImgIds(catIds=catIds)
-          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
-              width, height = im["width"], im["height"]
-              path = Path(im["file_name"])  # image filename
-              try:
-                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
-                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=False)
-                      for a in coco.loadAnns(annIds):
-                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
-                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
-                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
-                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
-              except Exception as e:
-                  print(e)

yolov5/data/SKU-110K.yaml

@@ -1,52 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
-# Example usage: python train.py --data SKU-110K.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── SKU-110K  ← downloads here (13.6 GB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/SKU-110K # dataset root dir
-train: train.txt # train images (relative to 'path')  8219 images
-val: val.txt # val images (relative to 'path')  588 images
-test: test.txt # test images (optional)  2936 images
-
-# Classes
-names:
-  0: object
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import shutil
-  from tqdm import tqdm
-  from utils.general import np, pd, Path, download, xyxy2xywh
-
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  parent = Path(dir.parent)  # download dir
-  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
-  download(urls, dir=parent, delete=False)
-
-  # Rename directories
-  if dir.exists():
-      shutil.rmtree(dir)
-  (parent / 'SKU110K_fixed').rename(dir)  # rename dir
-  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir
-
-  # Convert labels
-  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names
-  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
-      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations
-      images, unique_images = x[:, 0], np.unique(x[:, 0])
-      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
-          f.writelines(f'./images/{s}\n' for s in unique_images)
-      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
-          cls = 0  # single-class dataset
-          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
-              for r in x[images == im]:
-                  w, h = r[6], r[7]  # image width, height
-                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
-                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label

yolov5/data/VOC.yaml

@@ -1,99 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
-# Example usage: python train.py --data VOC.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── VOC  ← downloads here (2.8 GB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/VOC
-train: # train images (relative to 'path')  16551 images
-  - images/train2012
-  - images/train2007
-  - images/val2012
-  - images/val2007
-val: # val images (relative to 'path')  4952 images
-  - images/test2007
-test: # test images (optional)
-  - images/test2007
-
-# Classes
-names:
-  0: aeroplane
-  1: bicycle
-  2: bird
-  3: boat
-  4: bottle
-  5: bus
-  6: car
-  7: cat
-  8: chair
-  9: cow
-  10: diningtable
-  11: dog
-  12: horse
-  13: motorbike
-  14: person
-  15: pottedplant
-  16: sheep
-  17: sofa
-  18: train
-  19: tvmonitor
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import xml.etree.ElementTree as ET
-
-  from tqdm import tqdm
-  from utils.general import download, Path
-
-
-  def convert_label(path, lb_path, year, image_id):
-      def convert_box(size, box):
-          dw, dh = 1. / size[0], 1. / size[1]
-          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
-          return x * dw, y * dh, w * dw, h * dh
-
-      in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
-      out_file = open(lb_path, 'w')
-      tree = ET.parse(in_file)
-      root = tree.getroot()
-      size = root.find('size')
-      w = int(size.find('width').text)
-      h = int(size.find('height').text)
-
-      names = list(yaml['names'].values())  # names list
-      for obj in root.iter('object'):
-          cls = obj.find('name').text
-          if cls in names and int(obj.find('difficult').text) != 1:
-              xmlbox = obj.find('bndbox')
-              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
-              cls_id = names.index(cls)  # class id
-              out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
-
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
-  urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
-          f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
-          f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
-  download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
-
-  # Convert
-  path = dir / 'images/VOCdevkit'
-  for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
-      imgs_path = dir / 'images' / f'{image_set}{year}'
-      lbs_path = dir / 'labels' / f'{image_set}{year}'
-      imgs_path.mkdir(exist_ok=True, parents=True)
-      lbs_path.mkdir(exist_ok=True, parents=True)
-
-      with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
-          image_ids = f.read().strip().split()
-      for id in tqdm(image_ids, desc=f'{image_set}{year}'):
-          f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
-          lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path
-          f.rename(imgs_path / f.name)  # move image
-          convert_label(path, lb_path, year, id)  # convert labels to YOLO format

yolov5/data/VisDrone.yaml

@@ -1,69 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
-# Example usage: python train.py --data VisDrone.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── VisDrone  ← downloads here (2.3 GB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/VisDrone # dataset root dir
-train: VisDrone2019-DET-train/images # train images (relative to 'path')  6471 images
-val: VisDrone2019-DET-val/images # val images (relative to 'path')  548 images
-test: VisDrone2019-DET-test-dev/images # test images (optional)  1610 images
-
-# Classes
-names:
-  0: pedestrian
-  1: people
-  2: bicycle
-  3: car
-  4: van
-  5: truck
-  6: tricycle
-  7: awning-tricycle
-  8: bus
-  9: motor
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  from utils.general import download, os, Path
-
-  def visdrone2yolo(dir):
-      from PIL import Image
-      from tqdm import tqdm
-
-      def convert_box(size, box):
-          # Convert VisDrone box to YOLO xywh box
-          dw = 1. / size[0]
-          dh = 1. / size[1]
-          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
-
-      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
-      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
-      for f in pbar:
-          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
-          lines = []
-          with open(f, 'r') as file:  # read annotation.txt
-              for row in [x.split(',') for x in file.read().strip().splitlines()]:
-                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
-                      continue
-                  cls = int(row[5]) - 1
-                  box = convert_box(img_size, tuple(map(int, row[:4])))
-                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
-                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
-                      fl.writelines(lines)  # write label.txt
-
-
-  # Download
-  dir = Path(yaml['path'])  # dataset root dir
-  urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',
-          'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',
-          'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',
-          'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']
-  download(urls, dir=dir, curl=True, threads=4)
-
-  # Convert
-  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
-      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

yolov5/data/coco.yaml

@@ -1,115 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# COCO 2017 dataset http://cocodataset.org by Microsoft
-# Example usage: python train.py --data coco.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco  ← downloads here (20.1 GB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco # dataset root dir
-train: train2017.txt # train images (relative to 'path') 118287 images
-val: val2017.txt # val images (relative to 'path') 5000 images
-test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
-
-# Classes
-names:
-  0: person
-  1: bicycle
-  2: car
-  3: motorcycle
-  4: airplane
-  5: bus
-  6: train
-  7: truck
-  8: boat
-  9: traffic light
-  10: fire hydrant
-  11: stop sign
-  12: parking meter
-  13: bench
-  14: bird
-  15: cat
-  16: dog
-  17: horse
-  18: sheep
-  19: cow
-  20: elephant
-  21: bear
-  22: zebra
-  23: giraffe
-  24: backpack
-  25: umbrella
-  26: handbag
-  27: tie
-  28: suitcase
-  29: frisbee
-  30: skis
-  31: snowboard
-  32: sports ball
-  33: kite
-  34: baseball bat
-  35: baseball glove
-  36: skateboard
-  37: surfboard
-  38: tennis racket
-  39: bottle
-  40: wine glass
-  41: cup
-  42: fork
-  43: knife
-  44: spoon
-  45: bowl
-  46: banana
-  47: apple
-  48: sandwich
-  49: orange
-  50: broccoli
-  51: carrot
-  52: hot dog
-  53: pizza
-  54: donut
-  55: cake
-  56: chair
-  57: couch
-  58: potted plant
-  59: bed
-  60: dining table
-  61: toilet
-  62: tv
-  63: laptop
-  64: mouse
-  65: remote
-  66: keyboard
-  67: cell phone
-  68: microwave
-  69: oven
-  70: toaster
-  71: sink
-  72: refrigerator
-  73: book
-  74: clock
-  75: vase
-  76: scissors
-  77: teddy bear
-  78: hair drier
-  79: toothbrush
-
-# Download script/URL (optional)
-download: |
-  from utils.general import download, Path
-
-
-  # Download labels
-  segments = False  # segment or box labels
-  dir = Path(yaml['path'])  # dataset root dir
-  url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
-  urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
-  download(urls, dir=dir.parent)
-
-  # Download data
-  urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
-          'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
-          'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
-  download(urls, dir=dir / 'images', threads=3)

yolov5/data/coco128-seg.yaml

@@ -1,100 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
-# Example usage: python train.py --data coco128.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco128-seg  ← downloads here (7 MB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128-seg # dataset root dir
-train: images/train2017 # train images (relative to 'path') 128 images
-val: images/train2017 # val images (relative to 'path') 128 images
-test: # test images (optional)
-
-# Classes
-names:
-  0: person
-  1: bicycle
-  2: car
-  3: motorcycle
-  4: airplane
-  5: bus
-  6: train
-  7: truck
-  8: boat
-  9: traffic light
-  10: fire hydrant
-  11: stop sign
-  12: parking meter
-  13: bench
-  14: bird
-  15: cat
-  16: dog
-  17: horse
-  18: sheep
-  19: cow
-  20: elephant
-  21: bear
-  22: zebra
-  23: giraffe
-  24: backpack
-  25: umbrella
-  26: handbag
-  27: tie
-  28: suitcase
-  29: frisbee
-  30: skis
-  31: snowboard
-  32: sports ball
-  33: kite
-  34: baseball bat
-  35: baseball glove
-  36: skateboard
-  37: surfboard
-  38: tennis racket
-  39: bottle
-  40: wine glass
-  41: cup
-  42: fork
-  43: knife
-  44: spoon
-  45: bowl
-  46: banana
-  47: apple
-  48: sandwich
-  49: orange
-  50: broccoli
-  51: carrot
-  52: hot dog
-  53: pizza
-  54: donut
-  55: cake
-  56: chair
-  57: couch
-  58: potted plant
-  59: bed
-  60: dining table
-  61: toilet
-  62: tv
-  63: laptop
-  64: mouse
-  65: remote
-  66: keyboard
-  67: cell phone
-  68: microwave
-  69: oven
-  70: toaster
-  71: sink
-  72: refrigerator
-  73: book
-  74: clock
-  75: vase
-  76: scissors
-  77: teddy bear
-  78: hair drier
-  79: toothbrush
-
-# Download script/URL (optional)
-download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip

yolov5/data/coco128.yaml

@@ -1,100 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
-# Example usage: python train.py --data coco128.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco128  ← downloads here (7 MB)
-
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128 # dataset root dir
-train: images/train2017 # train images (relative to 'path') 128 images
-val: images/train2017 # val images (relative to 'path') 128 images
-test: # test images (optional)
-
-# Classes
-names:
-  0: person
-  1: bicycle
-  2: car
-  3: motorcycle
-  4: airplane
-  5: bus
-  6: train
-  7: truck
-  8: boat
-  9: traffic light
-  10: fire hydrant
-  11: stop sign
-  12: parking meter
-  13: bench
-  14: bird
-  15: cat
-  16: dog
-  17: horse
-  18: sheep
-  19: cow
-  20: elephant
-  21: bear
-  22: zebra
-  23: giraffe
-  24: backpack
-  25: umbrella
-  26: handbag
-  27: tie
-  28: suitcase
-  29: frisbee
-  30: skis
-  31: snowboard
-  32: sports ball
-  33: kite
-  34: baseball bat
-  35: baseball glove
-  36: skateboard
-  37: surfboard
-  38: tennis racket
-  39: bottle
-  40: wine glass
-  41: cup
-  42: fork
-  43: knife
-  44: spoon
-  45: bowl
-  46: banana
-  47: apple
-  48: sandwich
-  49: orange
-  50: broccoli
-  51: carrot
-  52: hot dog
-  53: pizza
-  54: donut
-  55: cake
-  56: chair
-  57: couch
-  58: potted plant
-  59: bed
-  60: dining table
-  61: toilet
-  62: tv
-  63: laptop
-  64: mouse
-  65: remote
-  66: keyboard
-  67: cell phone
-  68: microwave
-  69: oven
-  70: toaster
-  71: sink
-  72: refrigerator
-  73: book
-  74: clock
-  75: vase
-  76: scissors
-  77: teddy bear
-  78: hair drier
-  79: toothbrush
-
-# Download script/URL (optional)
-download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip

yolov5/data/hyps/hyp.Objects365.yaml

@@ -1,35 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Hyperparameters for Objects365 training
-# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
-# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.00258
-lrf: 0.17
-momentum: 0.779
-weight_decay: 0.00058
-warmup_epochs: 1.33
-warmup_momentum: 0.86
-warmup_bias_lr: 0.0711
-box: 0.0539
-cls: 0.299
-cls_pw: 0.825
-obj: 0.632
-obj_pw: 1.0
-iou_t: 0.2
-anchor_t: 3.44
-anchors: 3.2
-fl_gamma: 0.0
-hsv_h: 0.0188
-hsv_s: 0.704
-hsv_v: 0.36
-degrees: 0.0
-translate: 0.0902
-scale: 0.491
-shear: 0.0
-perspective: 0.0
-flipud: 0.0
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.0
-copy_paste: 0.0

yolov5/data/hyps/hyp.VOC.yaml

@@ -1,41 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Hyperparameters for VOC training
-# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
-# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
-
-# YOLOv5 Hyperparameter Evolution Results
-# Best generation: 467
-# Last generation: 996
-#    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss
-#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865
-
-lr0: 0.00334
-lrf: 0.15135
-momentum: 0.74832
-weight_decay: 0.00025
-warmup_epochs: 3.3835
-warmup_momentum: 0.59462
-warmup_bias_lr: 0.18657
-box: 0.02
-cls: 0.21638
-cls_pw: 0.5
-obj: 0.51728
-obj_pw: 0.67198
-iou_t: 0.2
-anchor_t: 3.3744
-fl_gamma: 0.0
-hsv_h: 0.01041
-hsv_s: 0.54703
-hsv_v: 0.27739
-degrees: 0.0
-translate: 0.04591
-scale: 0.75544
-shear: 0.0
-perspective: 0.0
-flipud: 0.0
-fliplr: 0.5
-mosaic: 0.85834
-mixup: 0.04266
-copy_paste: 0.0
-anchors: 3.412

yolov5/data/hyps/hyp.no-augmentation.yaml

@@ -1,36 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Hyperparameters when using Albumentations frameworks
-# python train.py --hyp hyp.no-augmentation.yaml
-# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
-
-lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937 # SGD momentum/Adam beta1
-weight_decay: 0.0005 # optimizer weight decay 5e-4
-warmup_epochs: 3.0 # warmup epochs (fractions ok)
-warmup_momentum: 0.8 # warmup initial momentum
-warmup_bias_lr: 0.1 # warmup initial bias lr
-box: 0.05 # box loss gain
-cls: 0.3 # cls loss gain
-cls_pw: 1.0 # cls BCELoss positive_weight
-obj: 0.7 # obj loss gain (scale with pixels)
-obj_pw: 1.0 # obj BCELoss positive_weight
-iou_t: 0.20 # IoU training threshold
-anchor_t: 4.0 # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-# this parameters are all zero since we want to use albumentation framework
-fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0 # image HSV-Hue augmentation (fraction)
-hsv_s: 0 # image HSV-Saturation augmentation (fraction)
-hsv_v: 0 # image HSV-Value augmentation (fraction)
-degrees: 0.0 # image rotation (+/- deg)
-translate: 0 # image translation (+/- fraction)
-scale: 0 # image scale (+/- gain)
-shear: 0 # image shear (+/- deg)
-perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0 # image flip up-down (probability)
-fliplr: 0.0 # image flip left-right (probability)
-mosaic: 0.0 # image mosaic (probability)
-mixup: 0.0 # image mixup (probability)
-copy_paste: 0.0 # segment copy-paste (probability)

yolov5/data/hyps/hyp.scratch-high.yaml

@@ -1,35 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Hyperparameters for high-augmentation COCO training from scratch
-# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937 # SGD momentum/Adam beta1
-weight_decay: 0.0005 # optimizer weight decay 5e-4
-warmup_epochs: 3.0 # warmup epochs (fractions ok)
-warmup_momentum: 0.8 # warmup initial momentum
-warmup_bias_lr: 0.1 # warmup initial bias lr
-box: 0.05 # box loss gain
-cls: 0.3 # cls loss gain
-cls_pw: 1.0 # cls BCELoss positive_weight
-obj: 0.7 # obj loss gain (scale with pixels)
-obj_pw: 1.0 # obj BCELoss positive_weight
-iou_t: 0.20 # IoU training threshold
-anchor_t: 4.0 # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4 # image HSV-Value augmentation (fraction)
-degrees: 0.0 # image rotation (+/- deg)
-translate: 0.1 # image translation (+/- fraction)
-scale: 0.9 # image scale (+/- gain)
-shear: 0.0 # image shear (+/- deg)
-perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0 # image flip up-down (probability)
-fliplr: 0.5 # image flip left-right (probability)
-mosaic: 1.0 # image mosaic (probability)
-mixup: 0.1 # image mixup (probability)
-copy_paste: 0.1 # segment copy-paste (probability)

yolov5/data/hyps/hyp.scratch-low.yaml

@@ -1,35 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Hyperparameters for low-augmentation COCO training from scratch
-# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937 # SGD momentum/Adam beta1
-weight_decay: 0.0005 # optimizer weight decay 5e-4
-warmup_epochs: 3.0 # warmup epochs (fractions ok)
-warmup_momentum: 0.8 # warmup initial momentum
-warmup_bias_lr: 0.1 # warmup initial bias lr
-box: 0.05 # box loss gain
-cls: 0.5 # cls loss gain
-cls_pw: 1.0 # cls BCELoss positive_weight
-obj: 1.0 # obj loss gain (scale with pixels)
-obj_pw: 1.0 # obj BCELoss positive_weight
-iou_t: 0.20 # IoU training threshold
-anchor_t: 4.0 # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4 # image HSV-Value augmentation (fraction)
-degrees: 0.0 # image rotation (+/- deg)
-translate: 0.1 # image translation (+/- fraction)
-scale: 0.5 # image scale (+/- gain)
-shear: 0.0 # image shear (+/- deg)
-perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0 # image flip up-down (probability)
-fliplr: 0.5 # image flip left-right (probability)
-mosaic: 1.0 # image mosaic (probability)
-mixup: 0.0 # image mixup (probability)
-copy_paste: 0.0 # segment copy-paste (probability)

yolov5/data/hyps/hyp.scratch-med.yaml

@@ -1,35 +0,0 @@
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Hyperparameters for medium-augmentation COCO training from scratch
-# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
-momentum: 0.937 # SGD momentum/Adam beta1
-weight_decay: 0.0005 # optimizer weight decay 5e-4
-warmup_epochs: 3.0 # warmup epochs (fractions ok)
-warmup_momentum: 0.8 # warmup initial momentum
-warmup_bias_lr: 0.1 # warmup initial bias lr
-box: 0.05 # box loss gain
-cls: 0.3 # cls loss gain
-cls_pw: 1.0 # cls BCELoss positive_weight
-obj: 0.7 # obj loss gain (scale with pixels)
-obj_pw: 1.0 # obj BCELoss positive_weight
-iou_t: 0.20 # IoU training threshold
-anchor_t: 4.0 # anchor-multiple threshold
-# anchors: 3  # anchors per output layer (0 to ignore)
-fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
-hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
-hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
-hsv_v: 0.4 # image HSV-Value augmentation (fraction)
-degrees: 0.0 # image rotation (+/- deg)
-translate: 0.1 # image translation (+/- fraction)
-scale: 0.9 # image scale (+/- gain)
-shear: 0.0 # image shear (+/- deg)
-perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
-flipud: 0.0 # image flip up-down (probability)
-fliplr: 0.5 # image flip left-right (probability)
-mosaic: 1.0 # image mosaic (probability)
-mixup: 0.1 # image mixup (probability)
-copy_paste: 0.0 # segment copy-paste (probability)

yolov5/data/scripts/download_weights.sh

@@ -1,23 +0,0 @@
-#!/bin/bash
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Download latest models from https://github.com/ultralytics/yolov5/releases
-# Example usage: bash data/scripts/download_weights.sh
-# parent
-# └── yolov5
-#     ├── yolov5s.pt  ← downloads here
-#     ├── yolov5m.pt
-#     └── ...
-
-python - <<EOF
-from utils.downloads import attempt_download
-
-p5 = list('nsmlx')  # P5 models
-p6 = [f'{x}6' for x in p5]  # P6 models
-cls = [f'{x}-cls' for x in p5]  # classification models
-seg = [f'{x}-seg' for x in p5]  # classification models
-
-for x in p5 + p6 + cls + seg:
-    attempt_download(f'weights/yolov5{x}.pt')
-
-EOF

yolov5/data/scripts/get_coco.sh

@@ -1,57 +0,0 @@
-#!/bin/bash
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Download COCO 2017 dataset http://cocodataset.org
-# Example usage: bash data/scripts/get_coco.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco  ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
-if [ "$#" -gt 0 ]; then
-  for opt in "$@"; do
-    case "${opt}" in
-    --train) train=true ;;
-    --val) val=true ;;
-    --test) test=true ;;
-    --segments) segments=true ;;
-    esac
-  done
-else
-  train=true
-  val=true
-  test=false
-  segments=false
-fi
-
-# Download/unzip labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-if [ "$segments" == "true" ]; then
-  f='coco2017labels-segments.zip' # 168 MB
-else
-  f='coco2017labels.zip' # 46 MB
-fi
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-
-# Download/unzip images
-d='../datasets/coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-if [ "$train" == "true" ]; then
-  f='train2017.zip' # 19G, 118k images
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$val" == "true" ]; then
-  f='val2017.zip' # 1G, 5k images
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$test" == "true" ]; then
-  f='test2017.zip' # 7G, 41k images (optional)
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-wait # finish background tasks

yolov5/data/scripts/get_coco128.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
-# Example usage: bash data/scripts/get_coco128.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco128  ← downloads here
-
-# Download/unzip images and labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco128.zip' # or 'coco128-segments.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-
-wait # finish background tasks

yolov5/data/scripts/get_imagenet.sh

@@ -1,52 +0,0 @@
-#!/bin/bash
-# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
-# Download ILSVRC2012 ImageNet dataset https://image-net.org
-# Example usage: bash data/scripts/get_imagenet.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── imagenet  ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
-if [ "$#" -gt 0 ]; then
-  for opt in "$@"; do
-    case "${opt}" in
-    --train) train=true ;;
-    --val) val=true ;;
-    esac
-  done
-else
-  train=true
-  val=true
-fi
-
-# Make dir
-d='../datasets/imagenet' # unzip directory
-mkdir -p $d && cd $d
-
-# Download/unzip train
-if [ "$train" == "true" ]; then
-  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
-  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
-  tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
-  find . -name "*.tar" | while read NAME; do
-    mkdir -p "${NAME%.tar}"
-    tar -xf "${NAME}" -C "${NAME%.tar}"
-    rm -f "${NAME}"
-  done
-  cd ..
-fi
-
-# Download/unzip val
-if [ "$val" == "true" ]; then
-  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
-  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
-  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
-fi
-
-# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
-# rm train/n04266014/n04266014_10835.JPEG
-
-# TFRecords (optional)
-# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt