Upload 150 files

requirements.txt

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+torch 
+torchvision
+opencv-python
+streamlit
+numpy

streamlitYolov.py

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+import streamlit as st
+import torch
+import cv2
+import numpy as np
+
+# Title and description of the app
+st.title("YOLOv5 Object Detection with Video Input")
+st.write("Live object detection from your webcam using YOLOv5!")
+
+# Load the pre-trained YOLOv5 model (COCO dataset)
+@st.cache_resource
+def load_model():
+    return torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+model = load_model()
+
+# Create a function to process video frames and apply YOLOv5
+def process_frame(frame, model):
+    # Perform inference
+    results = model(frame)
+
+    # Extract detections
+    detections = results.pandas().xyxy[0]
+
+    # Draw bounding boxes and labels on the frame
+    for _, row in detections.iterrows():
+        x1, y1, x2, y2 = int(row['xmin']), int(row['ymin']), int(row['xmax']), int(row['ymax'])
+        label = f"{row['name']} {row['confidence']:.2f}"
+        cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
+        cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (36, 255, 12), 2)
+
+    return frame
+
+# Start video capture
+run_video = st.checkbox("Start Webcam")
+
+if run_video:
+    # Initialize the webcam
+    cap = cv2.VideoCapture(0)  # 0 is the default camera
+
+    if not cap.isOpened():
+        st.error("Error: Could not open the webcam.")
+    else:
+        # Stream video
+        stframe = st.empty()  # Placeholder for displaying video frames
+
+        while run_video:
+            ret, frame = cap.read()
+
+            if not ret:
+                st.error("Error: Failed to capture video.")
+                break
+
+            # Process the frame with YOLOv5
+            processed_frame = process_frame(frame, model)
+
+            # Convert BGR to RGB for Streamlit
+            processed_frame = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB)
+
+            # Display the frame in Streamlit
+            stframe.image(processed_frame, channels="RGB", use_column_width=True)
+
+        cap.release()
+else:
+    st.write("Enable the checkbox above to start the webcam.")

yolov5/.dockerignore

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

yolov5/.gitattributes

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+# this drop notebooks from GitHub language stats
+*.ipynb linguist-vendored

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

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Dependabot for package version updates
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: pip
+    directory: "/"
+    schedule:
+      interval: weekly
+      time: "04:00"
+    open-pull-requests-limit: 10
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies
+
+  - package-ecosystem: github-actions
+    directory: "/.github/workflows"
+    schedule:
+      interval: weekly
+      time: "04:00"
+    open-pull-requests-limit: 5
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies

yolov5/.github/workflows/ci-testing.yml

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# YOLOv5 Continuous Integration (CI) GitHub Actions tests
+
+name: YOLOv5 CI
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+    branches: [master]
+  schedule:
+    - cron: "0 0 * * *" # runs at 00:00 UTC every day
+  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] # Add job names that you want to check for failure
+    if: always() # This ensures the job runs even if previous jobs fail
+    steps:
+      - name: Check for failure and notify
+        if: (needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure' || needs.Benchmarks.result == 'cancelled' || needs.Tests.result == 'cancelled') && github.repository == 'ultralytics/yolov5' && (github.event_name == 'schedule' || github.event_name == 'push')
+        uses: slackapi/slack-github-action@v1.27.0
+        with:
+          payload: |
+            {"text": "<!channel> GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"}
+        env:
+          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }}

yolov5/.github/workflows/cla.yml

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+# Ultralytics YOLO 🚀, AGPL-3.0 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/github-action@v2.5.1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          # Must be repository secret PAT
+          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_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/codeql-analysis.yml

@@ -0,0 +1,56 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities.
+# https://github.com/github/codeql-action
+
+name: "CodeQL"
+
+on:
+  schedule:
+    - cron: "0 0 1 * *" # Runs at 00:00 UTC on the 1st of every month
+  workflow_dispatch:
+
+jobs:
+  analyze:
+    name: Analyze
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        language: ["python"]
+        # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
+        # Learn more:
+        # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # Initializes the CodeQL tools for scanning.
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@v3
+        with:
+          languages: ${{ matrix.language }}
+          # If you wish to specify custom queries, you can do so here or in a config file.
+          # By default, queries listed here will override any specified in a config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
+          # queries: ./path/to/local/query, your-org/your-repo/queries@main
+
+      # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
+      # If this step fails, then you should remove it and run the build manually (see below)
+      - name: Autobuild
+        uses: github/codeql-action/autobuild@v3
+
+      # ℹ️ Command-line programs to run using the OS shell.
+      # 📚 https://git.io/JvXDl
+
+      # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
+      #    and modify them (or add more) to build your code if your project
+      #    uses a compiled language
+
+      #- run: |
+      #   make bootstrap
+      #   make release
+
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@v3

yolov5/.github/workflows/docker.yml

@@ -0,0 +1,60 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Builds ultralytics/yolov5:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+
+name: Publish Docker Images
+
+on:
+  push:
+    branches: [master]
+  workflow_dispatch:
+
+jobs:
+  docker:
+    if: github.repository == 'ultralytics/yolov5'
+    name: Push Docker image to Docker Hub
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0 # copy full .git directory to access full git history in Docker images
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Build and push arm64 image
+        uses: docker/build-push-action@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

@@ -0,0 +1,70 @@
+# 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_target:
+    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.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated
+          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_azure_api_key: ${{ secrets.OPENAI_AZURE_API_KEY }}
+          openai_azure_endpoint: ${{ secrets.OPENAI_AZURE_ENDPOINT }}
+          first_issue_response: |
+            👋 Hello @${{ 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/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
+            - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
+            - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+
+            ## Status
+
+            <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
+
+            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
+
+            ## Introducing YOLOv8 🚀
+
+            We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀!
+
+            Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
+
+            Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with:
+            ```bash
+            pip install ultralytics
+            ```

yolov5/.github/workflows/links.yml

@@ -0,0 +1,73 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee
+# Ignores the following status codes to reduce false positives:
+#   - 403(OpenVINO, 'forbidden')
+#   - 429(Instagram, 'too many requests')
+#   - 500(Zenodo, 'cached')
+#   - 502(Zenodo, 'bad gateway')
+#   - 999(LinkedIn, 'unknown status code')
+
+name: Check Broken links
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0 * * *" # runs at 00:00 UTC every day
+
+jobs:
+  Links:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Download and install lychee
+        run: |
+          LYCHEE_URL=$(curl -s https://api.github.com/repos/lycheeverse/lychee/releases/latest | grep "browser_download_url" | grep "x86_64-unknown-linux-gnu.tar.gz" | cut -d '"' -f 4)
+          curl -L $LYCHEE_URL -o lychee.tar.gz
+          tar xzf lychee.tar.gz
+          sudo mv lychee /usr/local/bin
+
+      - name: Test Markdown and HTML links with retry
+        uses: nick-invision/retry@v3
+        with:
+          timeout_minutes: 5
+          retry_wait_seconds: 60
+          max_attempts: 3
+          command: |
+            lychee \
+            --scheme 'https' \
+            --timeout 60 \
+            --insecure \
+            --accept 403,429,500,502,999 \
+            --exclude-all-private \
+            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
+            --exclude-path '**/ci.yaml' \
+            --github-token ${{ secrets.GITHUB_TOKEN }} \
+            --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'
+
+      - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
+        if: github.event_name == 'workflow_dispatch'
+        uses: nick-invision/retry@v3
+        with:
+          timeout_minutes: 5
+          retry_wait_seconds: 60
+          max_attempts: 3
+          command: |
+            lychee \
+            --scheme 'https' \
+            --timeout 60 \
+            --insecure \
+            --accept 429,999 \
+            --exclude-all-private \
+            --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \
+            --exclude-path '**/ci.yaml' \
+            --github-token ${{ secrets.GITHUB_TOKEN }} \
+            --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'

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

@@ -0,0 +1,71 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 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.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }}
+          GITHUB_REPOSITORY: ${{ github.repository }}

yolov5/.github/workflows/stale.yml

@@ -0,0 +1,47 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+name: Close stale issues
+on:
+  schedule:
+    - cron: "0 0 * * *" # Runs at 00:00 UTC every day
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v9
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+
+          stale-issue-message: |
+            👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
+
+            For additional resources and information, please see the links below:
+
+            - **Docs**: https://docs.ultralytics.com
+            - **HUB**: https://hub.ultralytics.com
+            - **Community**: https://community.ultralytics.com
+
+            Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
+
+            Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
+
+          stale-pr-message: |
+            👋 Hello there! We wanted to let you know that we've decided to close this pull request due to inactivity. We appreciate the effort you put into contributing to our project, but unfortunately, not all contributions are suitable or aligned with our product roadmap.
+
+            We hope you understand our decision, and please don't let it discourage you from contributing to open source projects in the future. We value all of our community members and their contributions, and we encourage you to keep exploring new projects and ways to get involved.
+
+            For additional resources and information, please see the links below:
+
+            - **Docs**: https://docs.ultralytics.com
+            - **HUB**: https://hub.ultralytics.com
+            - **Community**: https://community.ultralytics.com
+
+            Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
+
+          days-before-issue-stale: 30
+          days-before-issue-close: 10
+          days-before-pr-stale: 90
+          days-before-pr-close: 30
+          exempt-issue-labels: "documentation,tutorial,TODO"
+          operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.

yolov5/.gitignore

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

yolov5/CITATION.cff

@@ -0,0 +1,14 @@
+cff-version: 1.2.0
+preferred-citation:
+  type: software
+  message: If you use YOLOv5, please cite it as below.
+  authors:
+  - family-names: Jocher
+    given-names: Glenn
+    orcid: "https://orcid.org/0000-0001-5950-6979"
+  title: "YOLOv5 by Ultralytics"
+  version: 7.0
+  doi: 10.5281/zenodo.3908559
+  date-released: 2020-5-29
+  license: AGPL-3.0
+  url: "https://github.com/ultralytics/yolov5"

yolov5/CONTRIBUTING.md

@@ -0,0 +1,76 @@
+## 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

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
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+For more information on this, and how to apply and follow the GNU AGPL, see
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yolov5/README.md

@@ -0,0 +1,468 @@
+<div align="center">
+  <p>
+    <a href="https://ultralytics.com/events/yolovision" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></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"></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://ultralytics.com/discord"><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/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+  </div>
+  <br>
+
+YOLOv5 🚀 is the world's most loved vision AI, representing <a href="https://ultralytics.com">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
+
+We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href="https://docs.ultralytics.com/yolov5">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community 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%">
+  <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%">
+  <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%">
+  <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%">
+  <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%">
+  <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%">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+
+</div>
+<br>
+
+## <div align="center">YOLOv8 🚀 NEW</div>
+
+We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.
+
+See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with:
+
+[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
+
+```bash
+pip install ultralytics
+```
+
+<div align="center">
+  <a href="https://ultralytics.com/yolo" target="_blank">
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
+</div>
+
+## <div align="center">Documentation</div>
+
+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 repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+```bash
+git clone https://github.com/ultralytics/yolov5  # clone
+cd yolov5
+pip install -r requirements.txt  # install
+```
+
+</details>
+
+<details>
+<summary>Inference</summary>
+
+YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
+
+```python
+import torch
+
+# Model
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, custom
+
+# Images
+img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list
+
+# Inference
+results = model(img)
+
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+<details>
+<summary>Inference with detect.py</summary>
+
+`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
+
+```bash
+python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                               img.jpg                         # image
+                                               vid.mp4                         # video
+                                               screen                          # screenshot
+                                               path/                           # directory
+                                               list.txt                        # list of images
+                                               list.streams                    # list of streams
+                                               'path/*.jpg'                    # glob
+                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
+                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+```
+
+</details>
+
+<details>
+<summary>Training</summary>
+
+The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
+
+```bash
+python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
+                                                                 yolov5s                    64
+                                                                 yolov5m                    40
+                                                                 yolov5l                    24
+                                                                 yolov5x                    16
+```
+
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>
+
+<details open>
+<summary>Tutorials</summary>
+
+- [Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 RECOMMENDED
+- [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
+- [Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training)
+- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 NEW
+- [TFLite, ONNX, CoreML, TensorRT Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀
+- [NVIDIA Jetson platform Deployment](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 NEW
+- [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation)
+- [Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling)
+- [Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity)
+- [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution)
+- [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers)
+- [Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 NEW
+- [Roboflow for Datasets, Labeling, and Active Learning](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration)
+- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 NEW
+- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 NEW
+- [Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 NEW
+
+</details>
+
+## <div align="center">Integrations</div>
+
+<br>
+<a align="center" href="https://ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png"></a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov5-readme-comet2">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
+</div>
+
+|                                                           Roboflow                                                           |                                                 ClearML ⭐ NEW                                                  |                                                                        Comet ⭐ NEW                                                                        |                                          Neural Magic ⭐ NEW                                           |
+| :--------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Run YOLOv5 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
+
+## <div align="center">Ultralytics HUB</div>
+
+Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now!
+
+<a align="center" href="https://ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+
+## <div align="center">Why YOLOv5</div>
+
+YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 Figure</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
+</details>
+<details>
+  <summary>Figure Notes</summary>
+
+- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
+- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
+- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
+- **Reproduce** by `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
+
+| 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] | 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 are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `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.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+## <div align="center">Segmentation</div>
+
+Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials.
+
+<details>
+  <summary>Segmentation Checkpoints</summary>
+
+<div align="center">
+<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
+<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
+</div>
+
+We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy 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 are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
+- **Accuracy** values are for single-model single-scale on COCO dataset.<br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
+- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). <br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
+- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `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 auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`.
+
+```bash
+# Single-GPU
+python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
+
+# Multi-GPU 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 YOLOv5s-seg mask mAP on COCO dataset:
+
+```bash
+bash data/scripts/get_coco.sh --val --segments  # download COCO val segments split (780MB, 5000 images)
+python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # validate
+```
+
+### Predict
+
+Use pretrained YOLOv5m-seg.pt to predict bus.jpg:
+
+```bash
+python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load(
+    "ultralytics/yolov5", "custom", "yolov5m-seg.pt"
+)  # load from PyTorch Hub (WARNING: inference not yet supported)
+```
+
+| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
+| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+
+### Export
+
+Export YOLOv5s-seg model to ONNX and TensorRT:
+
+```bash
+python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
+```
+
+</details>
+
+## <div align="center">Classification</div>
+
+YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials.
+
+<details>
+  <summary>Classification Checkpoints</summary>
+
+<br>
+
+We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy 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 are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
+- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
+- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
+- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `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 auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
+
+```bash
+# Single-GPU
+python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
+
+# Multi-GPU DDP
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+```
+
+### Val
+
+Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
+
+```bash
+bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
+```
+
+### Predict
+
+Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
+
+```bash
+python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")  # load from PyTorch Hub
+```
+
+### Export
+
+Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
+
+```bash
+python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
+```
+
+</details>
+
+## <div align="center">Environments</div>
+
+Get started in seconds with our verified environments. Click each icon below for details.
+
+<div align="center">
+  <a href="https://bit.ly/yolov5-paperspace-notebook">
+    <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">
+    <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/ultralytics/yolov5">
+    <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">
+    <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/">
+    <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/">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
+</div>
+
+## <div align="center">Contribute</div>
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out the [YOLOv5 Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
+<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
+
+## <div align="center">License</div>
+
+Ultralytics offers two licensing options to accommodate diverse use cases:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for more details.
+- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+## <div align="center">Contact</div>
+
+For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues), and join our [Discord](https://discord.com/invite/ultralytics) community for questions and discussions!
+
+<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%">
+  <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%">
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+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
+  <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%">
+  <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%">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>
+
+[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation

yolov5/README.zh-CN.md

@@ -0,0 +1,468 @@
+<div align="center">
+  <p>
+    <a href="https://ultralytics.com/events/yolovision" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></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"></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://ultralytics.com/discord"><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/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+  </div>
+  <br>
+
+YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表<a href="https://ultralytics.com"> Ultralytics </a>对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。
+
+我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 <a href="https://docs.ultralytics.com/yolov5/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/yolov5/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://ultralytics.com/discord">Discord</a> 社区进行问题和讨论！
+
+如需申请企业许可，请在 [Ultralytics Licensing](https://www.ultralytics.com/license) 处填写表格
+
+<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%">
+  <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%">
+  <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%">
+  <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%">
+  <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%">
+  <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%">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
+</div>
+</div>
+
+## <div align="center">YOLOv8 🚀 新品</div>
+
+我们很高兴宣布 Ultralytics YOLOv8 🚀 的发布，这是我们新推出的领先水平、最先进的（SOTA）模型，发布于 **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**。 YOLOv8 旨在快速、准确且易于使用，使其成为广泛的物体检测、图像分割和图像分类任务的极佳选择。
+
+请查看 [YOLOv8 文档](https://docs.ultralytics.com)了解详细信息，并开始使用：
+
+[![PyPI 版本](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![下载量](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
+
+```commandline
+pip install ultralytics
+```
+
+<div align="center">
+  <a href="https://ultralytics.com/yolo" target="_blank">
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
+</div>
+
+## <div align="center">文档</div>
+
+有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com/yolov5/)。请参阅下面的快速入门示例。
+
+<details open>
+<summary>安装</summary>
+
+克隆 repo，并要求在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。
+
+```bash
+git clone https://github.com/ultralytics/yolov5  # clone
+cd yolov5
+pip install -r requirements.txt  # install
+```
+
+</details>
+
+<details>
+<summary>推理</summary>
+
+使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。
+
+```python
+import torch
+
+# Model
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, custom
+
+# Images
+img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list
+
+# Inference
+results = model(img)
+
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+<details>
+<summary>使用 detect.py 推理</summary>
+
+`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。
+
+```bash
+python detect.py --weights yolov5s.pt --source 0                               # webcam
+                                               img.jpg                         # image
+                                               vid.mp4                         # video
+                                               screen                          # screenshot
+                                               path/                           # directory
+                                               list.txt                        # list of images
+                                               list.streams                    # list of streams
+                                               'path/*.jpg'                    # glob
+                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
+                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+```
+
+</details>
+
+<details>
+<summary>训练</summary>
+
+下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data)
+将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快）。 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。
+
+```bash
+python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
+                                                                 yolov5s                    64
+                                                                 yolov5m                    40
+                                                                 yolov5l                    24
+                                                                 yolov5x                    16
+```
+
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>
+
+<details open>
+<summary>教程</summary>
+
+- [训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 推荐
+- [获得最佳训练结果的技巧](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
+- [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training)
+- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 新
+- [TFLite，ONNX，CoreML，TensorRT导出](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀
+- [NVIDIA Jetson平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 新
+- [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation)
+- [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling)
+- [模型剪枝/稀疏](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity)
+- [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution)
+- [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers)
+- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 新
+- [Roboflow用于数据集、标注和主动学习](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration)
+- [ClearML日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 新
+- [使用Neural Magic的Deepsparse的YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 新
+- [Comet日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 新
+
+</details>
+
+## <div align="center">模块集成</div>
+
+<br>
+<a align="center" href="https://ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png"></a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov5-readme-comet2">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
+</div>
+
+|                                                  Roboflow                                                  |                               ClearML ⭐ 新                                |                                                   Comet ⭐ 新                                                    |                                           Neural Magic ⭐ 新                                           |
+| :--------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| 将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv5 [ClearML](https://clear.ml/)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv5 推理的速度最高可提高6倍 |
+
+## <div align="center">Ultralytics HUB</div>
+
+[Ultralytics HUB](https://www.ultralytics.com/hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！
+
+<a align="center" href="https://ultralytics.com/hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+
+## <div align="center">为什么选择 YOLOv5</div>
+
+YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结果。
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 图</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
+</details>
+<details>
+  <summary>图表笔记</summary>
+
+- **COCO AP val** 表示 mAP@0.5:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。
+- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例，batchsize 为 32 。
+- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。
+- **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+
+</details>
+
+### 预训练模型
+
+| 模型                                                                                           | 尺寸<br><sup>（像素） | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 推理速度<br><sup>CPU b1<br>（ms） | 推理速度<br><sup>V100 b1<br>（ms） | 速度<br><sup>V100 b32<br>（ms） | 参数量<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] | 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>笔记</summary>
+
+- 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。
+- \*\*mAP<sup>val</sup>\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。<br>复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。<br>复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+- **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) 包括反射和尺度变换。<br>复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+## <div align="center">实例分割模型 ⭐ 新</div>
+
+我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。
+
+<details>
+  <summary>实例分割模型列表</summary>
+
+<br>
+
+<div align="center">
+<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
+<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
+</div>
+
+我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。
+
+| 模型                                                                                       | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时长<br><sup>300 epochs<br>A100 GPU（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TRT A100<br>（ms） | 参数量<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                  |
+
+- 所有模型使用 SGD 优化器训练， 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数， 图像大小为 640 。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official
+- **准确性**结果都在 COCO 数据集上，使用单模型单尺度测试得到。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
+- **推理速度**是使用 100 张图像推理时间进行平均得到，测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速��（NMS 每张图像增加约 1 毫秒）。<br>复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
+- **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.<br>运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
+
+</details>
+
+<details>
+  <summary>分割模型使用示例 &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="Open In Colab"></a></summary>
+
+### 训练
+
+YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集，用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载，使用命令 `bash data/scripts/get_coco.sh --train --val --segments`， 在下载完毕后，使用命令 `python train.py --data coco.yaml` 开启训练。
+
+```bash
+# 单 GPU
+python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
+
+# 多 GPU， DDP 模式
+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 mask mAP：
+
+```bash
+bash data/scripts/get_coco.sh --val --segments  # 下载 COCO val segments 数据集 (780MB, 5000 images)
+python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # 验证
+```
+
+### 预测
+
+使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg：
+
+```bash
+python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load(
+    "ultralytics/yolov5", "custom", "yolov5m-seg.pt"
+)  # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持)
+```
+
+| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
+| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
+
+### 模型导出
+
+将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT：
+
+```bash
+python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
+```
+
+</details>
+
+## <div align="center">分类网络 ⭐ 新</div>
+
+YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持！详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。
+
+<details>
+  <summary>分类网络模型</summary>
+
+<br>
+
+我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。
+
+| 模型                                                                                               | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 训练时长<br><sup>90 epochs<br>4xA100（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TensorRT V100<br>（ms） | 参数<br><sup>(M) | FLOPs<br><sup>@640 (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                    |
+| [Resnetzch](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 (点击以展开)</summary>
+
+- 所有模型都使用 SGD 优化器训练 90 个 epochs，都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数， 图像大小为 224 ，且都使用默认设置。<br>训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
+- **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224`
+- **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。<br>复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
+- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。<br>复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
+</details>
+</details>
+
+<details>
+  <summary>分类训练示例 &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="Open In Colab"></a></summary>
+
+### 训练
+
+YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集，命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。
+
+```bash
+# 单 GPU
+python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
+
+# 多 GPU， DDP 模式
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+```
+
+### 验证
+
+在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性：
+
+```bash
+bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
+```
+
+### 预测
+
+使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg：
+
+```bash
+python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
+```
+
+```python
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")  # load from PyTorch Hub
+```
+
+### 模型导出
+
+将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT：
+
+```bash
+python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
+```
+
+</details>
+
+## <div align="center">环境</div>
+
+使用下面我们经过验证的环境，在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。
+
+<div align="center">
+  <a href="https://bit.ly/yolov5-paperspace-notebook">
+    <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">
+    <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/ultralytics/yolov5">
+    <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">
+    <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/">
+    <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/">
+    <img src="https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png" width="10%" /></a>
+</div>
+
+## <div align="center">贡献</div>
+
+我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/)，并填写 [YOLOv5调查](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
+<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
+
+## <div align="center">许可证</div>
+
+Ultralytics 提供两种许可证选项以适应各种使用场景：
+
+- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/license)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件以了解更多细节。
+- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://www.ultralytics.com/license)与我们联系。
+
+## <div align="center">联系方式</div>
+
+对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)，并加入我们的 [Discord](https://discord.com/invite/ultralytics) 社区进行问题和讨论！
+
+<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%">
+  <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%">
+  <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%">
+  <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%">
+  <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%">
+  <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%">
+  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+</div>
+
+[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation

yolov5/benchmarks.py

@@ -0,0 +1,294 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,241 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,382 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,1488 @@
+{
+ "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/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/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

@@ -0,0 +1,178 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,72 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,52 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,1020 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,30 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,119 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,1020 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,436 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,51 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,98 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,68 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,114 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,99 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# COCO128-seg dataset https://www.kaggle.com/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

@@ -0,0 +1,99 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# COCO128 dataset https://www.kaggle.com/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

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

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+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,35 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,34 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,34 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,34 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 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

@@ -0,0 +1,22 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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