Upload 142 files

yolov5/.github/ISSUE_TEMPLATE/config.yml

@@ -7,5 +7,5 @@ contact_links:
     url: https://community.ultralytics.com/
     about: Ask on Ultralytics Community Forum
   - name: 🎧 Discord
-    url: https://discord.gg/2wNGbc6g9X
+    url: https://ultralytics.com/discord
     about: Ask on Ultralytics Discord

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

@@ -21,7 +21,7 @@ jobs:
         python-version: [ '3.10' ]  # requires python<=3.10
         model: [ yolov5n ]
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
@@ -63,11 +63,11 @@ jobs:
             python-version: '3.9'
             model: yolov5n
           - os: ubuntu-latest
-            python-version: '3.8'  # torch 1.7.0 requires python >=3.6, <=3.8
+            python-version: '3.8'  # torch 1.8.0 requires python >=3.6, <=3.8
             model: yolov5n
-            torch: '1.7.0'  # min torch version CI https://pypi.org/project/torchvision/
+            torch: '1.8.0'  # min torch version CI https://pypi.org/project/torchvision/
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
@@ -75,8 +75,8 @@ jobs:
       - name: Install requirements
         run: |
           python -m pip install --upgrade pip wheel
-          if [ "${{ matrix.torch }}" == "1.7.0" ]; then
-              pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu
+          if [ "${{ matrix.torch }}" == "1.8.0" ]; then
+              pip install -r requirements.txt torch==1.8.0 torchvision==0.9.0 --extra-index-url https://download.pytorch.org/whl/cpu
           else
               pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
           fi

yolov5/.github/workflows/codeql-analysis.yml

@@ -23,7 +23,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       # Initializes the CodeQL tools for scanning.
       - name: Initialize CodeQL

yolov5/.github/workflows/docker.yml

@@ -15,22 +15,24 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
-        uses: actions/checkout@v3
+        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@v2
+        uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
 
       - name: Login to Docker Hub
-        uses: docker/login-action@v2
+        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@v4
+        uses: docker/build-push-action@v5
         continue-on-error: true
         with:
           context: .
@@ -40,7 +42,7 @@ jobs:
           tags: ultralytics/yolov5:latest-arm64
 
       - name: Build and push CPU image
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v5
         continue-on-error: true
         with:
           context: .
@@ -49,7 +51,7 @@ jobs:
           tags: ultralytics/yolov5:latest-cpu
 
       - name: Build and push GPU image
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v5
         continue-on-error: true
         with:
           context: .

yolov5/.github/workflows/greetings.yml

@@ -31,7 +31,7 @@ jobs:
 
             ## Requirements
 
-            [**Python>=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started:
+            [**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

yolov5/.github/workflows/links.yml

@@ -1,6 +1,11 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# YOLO Continuous Integration (CI) GitHub Actions tests broken link checker
-# Accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter)
+# 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
 
@@ -13,7 +18,7 @@ jobs:
   Links:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Download and install lychee
         run: |
@@ -28,7 +33,7 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
+          command: lychee --accept 403,429,500,502,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html'
 
       - name: Test Markdown, HTML, YAML, Python and Notebook links with retry
         if: github.event_name == 'workflow_dispatch'
@@ -37,4 +42,4 @@ jobs:
           timeout_minutes: 5
           retry_wait_seconds: 60
           max_attempts: 3
-          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
+          command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'

yolov5/.github/workflows/translate-readme.yml

@@ -14,9 +14,9 @@ jobs:
   Translate:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Setup Node.js
-        uses: actions/setup-node@v3
+        uses: actions/setup-node@v4
         with:
           node-version: 16
       # ISO Language Codes: https://cloud.google.com/translate/docs/languages

yolov5/.pre-commit-config.yaml

@@ -11,7 +11,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -22,7 +22,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.8.0
+    rev: v3.15.0
     hooks:
       - id: pyupgrade
         name: Upgrade code
@@ -34,13 +34,13 @@ repos:
         name: Sort imports
 
   - repo: https://github.com/google/yapf
-    rev: v0.40.0
+    rev: v0.40.2
     hooks:
       - id: yapf
         name: YAPF formatting
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.16
+    rev: 0.7.17
     hooks:
       - id: mdformat
         name: MD formatting
@@ -50,13 +50,13 @@ repos:
         # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 6.1.0
     hooks:
       - id: flake8
         name: PEP8
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
       - id: codespell
         args:

yolov5/README.md

@@ -1,7 +1,11 @@
 <div align="center">
   <p>
+    <a href="https://yolovision.ultralytics.com/" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
+    <!--
     <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
       <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png"></a>
+    -->
   </p>
 
 [English](README.md) | [简体中文](README.zh-CN.md)
@@ -20,31 +24,24 @@
 
 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://discord.gg/2wNGbc6g9X">Discord</a> community for questions and discussions!
+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://ultralytics.com/license).
 
 <div align="center">
-  <a href="https://github.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://discord.gg/2wNGbc6g9X" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
+  <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"><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://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="Ultralytics Instagram"></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>
@@ -52,10 +49,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
 
 ## <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.
+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:
 
@@ -78,8 +72,8 @@ See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) for full documentatio
 <summary>Install</summary>
 
 Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
-[**Python>=3.7.0**](https://www.python.org/) environment, including
-[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+[**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
@@ -92,8 +86,7 @@ pip install -r requirements.txt  # install
 <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).
+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
@@ -116,8 +109,7 @@ results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
 <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`.
+`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
@@ -128,7 +120,7 @@ python detect.py --weights yolov5s.pt --source 0                               #
                                                list.txt                        # list of images
                                                list.streams                    # list of streams
                                                'path/*.jpg'                    # glob
-                                               'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
                                                'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 ```
 
@@ -139,11 +131,7 @@ python detect.py --weights yolov5s.pt --source 0                               #
 
 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.
+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
@@ -461,37 +449,30 @@ We love your input! We want to make contributing to YOLOv5 as easy and transpare
 
 ## <div align="center">License</div>
 
-YOLOv5 is available under two different licenses:
+Ultralytics offers two licensing options to accommodate diverse use cases:
 
-- **AGPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.
-- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) 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://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.gg/2wNGbc6g9X) community for questions and discussions!
+For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues), and join our [Discord](https://ultralytics.com/discord) community for questions and discussions!
 
 <br>
 <div align="center">
-  <a href="https://github.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://discord.gg/2wNGbc6g9X" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
+  <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"><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://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></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

@@ -1,7 +1,11 @@
 <div align="center">
   <p>
+    <a href="https://yolovision.ultralytics.com/" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
+    <!--
     <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
       <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png"></a>
+    -->
   </p>
 
 [英文](README.md)|[简体中文](README.zh-CN.md)<br>
@@ -19,38 +23,30 @@
 
 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/">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://discord.gg/2wNGbc6g9X">Discord</a> 社区进行问题和讨论！
+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://ultralytics.com/license) 处填写表格
 
 <div align="center">
-  <a href="https://github.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://discord.gg/2wNGbc6g9X" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
+  <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"><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://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="Ultralytics Instagram"></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。
+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：
 
@@ -67,12 +63,12 @@ pip install ultralytics
 
 ## <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)。请参阅下面的快速入门示例。
+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>=3.7.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/) 。
+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
@@ -85,8 +81,7 @@ pip install -r requirements.txt  # install
 <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。
+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
@@ -109,8 +104,7 @@ results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
 <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` 。
+`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
@@ -121,7 +115,7 @@ python detect.py --weights yolov5s.pt --source 0                               #
                                                list.txt                        # list of images
                                                list.streams                    # list of streams
                                                'path/*.jpg'                    # glob
-                                               'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                               'https://youtu.be/LNwODJXcvt4'  # YouTube
                                                'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 ```
 
@@ -130,12 +124,8 @@ python detect.py --weights yolov5s.pt --source 0                               #
 <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。
+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
@@ -250,7 +240,7 @@ YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结
 
 </details>
 
-## <div align="center">实例分割模型 ⭐  新</div>
+## <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。
 
@@ -452,39 +442,32 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu
 <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>
+## <div align="center">许可证</div>
 
-YOLOv5 在两种不同的 License 下可用：
+Ultralytics 提供两种许可证选项以适应各种使用场景：
 
-- **AGPL-3.0 License**： 查看 [License](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件的详细信息。
-- **企业License**：在没有 AGPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。
+- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)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://ultralytics.com/license)与我们联系。
 
-## <div align="center">联系我们</div>
+## <div align="center">联系方式</div>
 
-对于 YOLOv5 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.gg/2wNGbc6g9X) 社区进行问题和讨论！
+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://ultralytics.com/discord) 社区进行问题和讨论！
 
 <br>
 <div align="center">
-  <a href="https://github.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://discord.gg/2wNGbc6g9X" style="text-decoration:none;">
-    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
+  <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"><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://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></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/classify/predict.py

@@ -11,7 +11,7 @@ Usage - sources:
                                                                    list.txt                        # list of images
                                                                    list.streams                    # list of streams
                                                                    'path/*.jpg'                    # glob
-                                                                   'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                                   'https://youtu.be/LNwODJXcvt4'  # YouTube
                                                                    'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 
 Usage - formats:
@@ -43,12 +43,13 @@ 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.plots import Annotator
 from utils.torch_utils import select_device, smart_inference_mode
 
 
@@ -144,7 +145,7 @@ def run(
             # 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))
+                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')

yolov5/classify/tutorial.ipynb

@@ -87,7 +87,7 @@
         "                              screen  # screenshot\n",
         "                              path/  # directory\n",
         "                              'path/*.jpg'  # glob\n",
-        "                              'https://youtu.be/Zgi9g1ksQHc'  # YouTube\n",
+        "                              'https://youtu.be/LNwODJXcvt4'  # YouTube\n",
         "                              'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",
         "```"
       ]
@@ -1445,7 +1445,7 @@
         "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
         "import torch\n",
         "\n",
-        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # yolov5n - yolov5x6 or custom\n",
+        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True)  # or yolov5n - yolov5x6 or custom\n",
         "im = 'https://ultralytics.com/images/zidane.jpg'  # file, Path, PIL.Image, OpenCV, nparray, list\n",
         "results = model(im)  # inference\n",
         "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."

yolov5/detect.py

@@ -11,7 +11,7 @@ Usage - sources:
                                                      list.txt                        # list of images
                                                      list.streams                    # list of streams
                                                      'path/*.jpg'                    # glob
-                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                     'https://youtu.be/LNwODJXcvt4'  # YouTube
                                                      'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 
 Usage - formats:
@@ -29,6 +29,7 @@ Usage - formats:
 """
 
 import argparse
+import csv
 import os
 import platform
 import sys
@@ -42,11 +43,12 @@ if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
+
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
 from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                            increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
-from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import select_device, smart_inference_mode
 
 
@@ -62,6 +64,7 @@ def run(
         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
         view_img=False,  # show results
         save_txt=False,  # save results to *.txt
+        save_csv=False,  # save results in CSV format
         save_conf=False,  # save confidences in --save-txt labels
         save_crop=False,  # save cropped prediction boxes
         nosave=False,  # do not save images/videos
@@ -134,6 +137,18 @@ def run(
         # Second-stage classifier (optional)
         # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
 
+        # Define the path for the CSV file
+        csv_path = save_dir / 'predictions.csv'
+
+        # Create or append to the CSV file
+        def write_to_csv(image_name, prediction, confidence):
+            data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence}
+            with open(csv_path, mode='a', newline='') as f:
+                writer = csv.DictWriter(f, fieldnames=data.keys())
+                if not csv_path.is_file():
+                    writer.writeheader()
+                writer.writerow(data)
+
         # Process predictions
         for i, det in enumerate(pred):  # per image
             seen += 1
@@ -161,6 +176,14 @@ def run(
 
                 # Write results
                 for *xyxy, conf, cls in reversed(det):
+                    c = int(cls)  # integer class
+                    label = names[c] if hide_conf else f'{names[c]}'
+                    confidence = float(conf)
+                    confidence_str = f'{confidence:.2f}'
+
+                    if save_csv:
+                        write_to_csv(p.name, label, confidence_str)
+
                     if save_txt:  # Write to file
                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                         line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
@@ -228,6 +251,7 @@ def parse_opt():
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--view-img', action='store_true', help='show results')
     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-csv', action='store_true', help='save results in CSV format')
     parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
     parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
     parser.add_argument('--nosave', action='store_true', help='do not save images/videos')

yolov5/export.py

@@ -155,7 +155,7 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
     import onnx
 
     LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
-    f = file.with_suffix('.onnx')
+    f = str(file.with_suffix('.onnx'))
 
     output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
     if dynamic:
@@ -207,7 +207,7 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
 @try_export
 def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:')):
     # YOLOv5 OpenVINO export
-    check_requirements('openvino-dev>=2022.3')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+    check_requirements('openvino-dev>=2023.0')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
     import openvino.runtime as ov  # noqa
     from openvino.tools import mo  # noqa
 
@@ -216,7 +216,7 @@ def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:
     f_onnx = file.with_suffix('.onnx')
     f_ov = str(Path(f) / file.with_suffix('.xml').name)
     if int8:
-        check_requirements('nncf')
+        check_requirements('nncf>=2.4.0')  # requires at least version 2.4.0 to use the post-training quantization
         import nncf
         import numpy as np
         from openvino.runtime import Core

yolov5/models/common.py

@@ -24,12 +24,24 @@ import torch.nn as nn
 from PIL import Image
 from torch.cuda import amp
 
+# Import 'ultralytics' package or install if if missing
+try:
+    import ultralytics
+
+    assert hasattr(ultralytics, '__version__')  # verify package is not directory
+except (ImportError, AssertionError):
+    import os
+
+    os.system('pip install -U ultralytics')
+    import ultralytics
+
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
+
 from utils import TryExcept
 from utils.dataloaders import exif_transpose, letterbox
 from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
                            increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
                            xyxy2xywh, yaml_load)
-from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import copy_attr, smart_inference_mode
 
 
@@ -373,18 +385,18 @@ class DetectMultiBackend(nn.Module):
                 stride, names = int(meta['stride']), eval(meta['names'])
         elif xml:  # OpenVINO
             LOGGER.info(f'Loading {w} for OpenVINO inference...')
-            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+            check_requirements('openvino>=2023.0')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
             from openvino.runtime import Core, Layout, get_batch
-            ie = Core()
+            core = Core()
             if not Path(w).is_file():  # if not *.xml
                 w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
-            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
-            if network.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout('NCHW'))
-            batch_dim = get_batch(network)
+            ov_model = core.read_model(model=w, weights=Path(w).with_suffix('.bin'))
+            if ov_model.get_parameters()[0].get_layout().empty:
+                ov_model.get_parameters()[0].set_layout(Layout('NCHW'))
+            batch_dim = get_batch(ov_model)
             if batch_dim.is_static:
                 batch_size = batch_dim.get_length()
-            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for Intel NCS2
+            ov_compiled_model = core.compile_model(ov_model, device_name='AUTO')  # AUTO selects best available device
             stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
@@ -524,7 +536,7 @@ class DetectMultiBackend(nn.Module):
             y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
         elif self.xml:  # OpenVINO
             im = im.cpu().numpy()  # FP32
-            y = list(self.executable_network([im]).values())
+            y = list(self.ov_compiled_model(im).values())
         elif self.engine:  # TensorRT
             if self.dynamic and im.shape != self.bindings['images'].shape:
                 i = self.model.get_binding_index('images')

yolov5/models/experimental.py

@@ -87,11 +87,11 @@ def attempt_load(weights, device=None, inplace=True, fuse=True):
 
         model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode
 
-    # Module compatibility updates
+    # Module updates
     for m in model.modules():
         t = type(m)
         if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
-            m.inplace = inplace  # torch 1.7.0 compatibility
+            m.inplace = inplace
             if t is Detect and not isinstance(m.anchor_grid, list):
                 delattr(m, 'anchor_grid')
                 setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)

yolov5/requirements.txt

@@ -4,18 +4,18 @@
 # Base ------------------------------------------------------------------------
 gitpython>=3.1.30
 matplotlib>=3.3
-numpy>=1.18.5
+numpy>=1.22.2
 opencv-python>=4.1.1
-Pillow>=7.1.2
+Pillow>=10.0.1
 psutil  # system resources
 PyYAML>=5.3.1
 requests>=2.23.0
 scipy>=1.4.1
 thop>=0.1.1  # FLOPs computation
-torch>=1.7.0  # see https://pytorch.org/get-started/locally (recommended)
-torchvision>=0.8.1
+torch>=1.8.0  # see https://pytorch.org/get-started/locally (recommended)
+torchvision>=0.9.0
 tqdm>=4.64.0
-ultralytics>=8.0.111
+ultralytics>=8.0.147
 # protobuf<=3.20.1  # https://github.com/ultralytics/yolov5/issues/8012
 
 # Logging ---------------------------------------------------------------------
@@ -36,7 +36,7 @@ seaborn>=0.11.0
 # scikit-learn<=1.1.2  # CoreML quantization
 # tensorflow>=2.4.0  # TF exports (-cpu, -aarch64, -macos)
 # tensorflowjs>=3.9.0  # TF.js export
-# openvino-dev  # OpenVINO export
+# openvino-dev>=2023.0  # OpenVINO export
 
 # Deploy ----------------------------------------------------------------------
 setuptools>=65.5.1 # Snyk vulnerability fix

yolov5/segment/predict.py

@@ -11,7 +11,7 @@ Usage - sources:
                                                                   list.txt                        # list of images
                                                                   list.streams                    # list of streams
                                                                   'path/*.jpg'                    # glob
-                                                                  'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                                  'https://youtu.be/LNwODJXcvt4'  # YouTube
                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 
 Usage - formats:
@@ -42,12 +42,13 @@ if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
+from ultralytics.utils.plotting import Annotator, colors, save_one_box
+
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
 from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                            increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
                            strip_optimizer)
-from utils.plots import Annotator, colors, save_one_box
 from utils.segment.general import masks2segments, process_mask, process_mask_native
 from utils.torch_utils import select_device, smart_inference_mode
 

yolov5/segment/tutorial.ipynb

@@ -87,7 +87,7 @@
         "                             screen  # screenshot\n",
         "                             path/  # directory\n",
         "                             'path/*.jpg'  # glob\n",
-        "                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube\n",
+        "                             'https://youtu.be/LNwODJXcvt4'  # YouTube\n",
         "                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",
         "```"
       ]
@@ -558,7 +558,7 @@
         "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
         "import torch\n",
         "\n",
-        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg')  # yolov5n - yolov5x6 or custom\n",
+        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg', force_reload=True, trust_repo=True)  # or yolov5n - yolov5x6 or custom\n",
         "im = 'https://ultralytics.com/images/zidane.jpg'  # file, Path, PIL.Image, OpenCV, nparray, list\n",
         "results = model(im)  # inference\n",
         "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."

yolov5/tutorial.ipynb

@@ -31,7 +31,7 @@
         "  <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>We hope that the resources in this notebook 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\">GitHub</a> for support, and join our <a href=\"https://discord.gg/2wNGbc6g9X\">Discord</a> community for questions and discussions!\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>We hope that the resources in this notebook 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\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
         "\n",
         "</div>"
       ]
@@ -65,7 +65,7 @@
         "import utils\n",
         "display = utils.notebook_init()  # checks"
       ],
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -95,12 +95,12 @@
         "\n",
         "```shell\n",
         "python detect.py --source 0  # webcam\n",
-        "                          img.jpg  # image \n",
+        "                          img.jpg  # image\n",
         "                          vid.mp4  # video\n",
         "                          screen  # screenshot\n",
         "                          path/  # directory\n",
         "                         'path/*.jpg'  # glob\n",
-        "                         'https://youtu.be/Zgi9g1ksQHc'  # YouTube\n",
+        "                         'https://youtu.be/LNwODJXcvt4'  # YouTube\n",
         "                         'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",
         "```"
       ]
@@ -118,7 +118,7 @@
         "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
         "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
       ],
-      "execution_count": 13,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -174,7 +174,7 @@
         "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download (780M - 5000 images)\n",
         "!unzip -q tmp.zip -d ../datasets && rm tmp.zip  # unzip"
       ],
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -198,7 +198,7 @@
         "# Validate YOLOv5s on COCO val\n",
         "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
       ],
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -308,7 +308,7 @@
         "# Train YOLOv5s on COCO128 for 3 epochs\n",
         "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
       ],
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -539,7 +539,7 @@
         "\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",
+        "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"
       ]
@@ -593,7 +593,7 @@
         "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
         "import torch\n",
         "\n",
-        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)  # yolov5n - yolov5x6 or custom\n",
+        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True)  # or yolov5n - yolov5x6 or custom\n",
         "im = 'https://ultralytics.com/images/zidane.jpg'  # file, Path, PIL.Image, OpenCV, nparray, list\n",
         "results = model(im)  # inference\n",
         "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."

yolov5/utils/__init__.py

@@ -54,7 +54,7 @@ def notebook_init(verbose=True):
     import os
     import shutil
 
-    from ultralytics.yolo.utils.checks import check_requirements
+    from ultralytics.utils.checks import check_requirements
 
     from utils.general import check_font, is_colab
     from utils.torch_utils import select_device  # imports

yolov5/utils/dataloaders.py

@@ -355,7 +355,7 @@ class LoadStreams:
             # Start thread to read frames from video stream
             st = f'{i + 1}/{n}: {s}... '
             if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'):  # if source is YouTube video
-                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
+                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
                 check_requirements(('pafy', 'youtube_dl==2020.12.2'))
                 import pafy
                 s = pafy.new(s).getbest(preftype='mp4').url  # YouTube URL

yolov5/utils/docker/Dockerfile

@@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg
 # RUN alias python=python3
 
 # Security updates
@@ -24,14 +24,13 @@ RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+COPY . /usr/src/app
 
 # Install pip packages
 COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
 RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
-    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3'
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'
     # tensorflow tensorflowjs \
 
 # Set environment variables

yolov5/utils/docker/Dockerfile-arm64

@@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 ENV DEBIAN_FRONTEND noninteractive
 RUN apt update
 RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev
+RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev
 # RUN alias python=python3
 
 # Install pip packages
@@ -27,8 +27,7 @@ RUN mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+COPY . /usr/src/app
 ENV DEBIAN_FRONTEND teletype
 
 

yolov5/utils/docker/Dockerfile-cpu

@@ -3,23 +3,25 @@
 # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
 
 # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:22.10
+FROM ubuntu:mantic-20231011
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
-ENV DEBIAN_FRONTEND noninteractive
-RUN apt update
-RUN TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
 # RUN alias python=python3
 
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
 # Install pip packages
 COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
 RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
-    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' \
+    coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \
     # tensorflow tensorflowjs \
     --extra-index-url https://download.pytorch.org/whl/cpu
 
@@ -28,9 +30,7 @@ RUN mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-# COPY . /usr/src/app  (issues as not a .git directory)
-RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
-ENV DEBIAN_FRONTEND teletype
+COPY . /usr/src/app
 
 
 # Usage Examples -------------------------------------------------------------------------------------------------------

yolov5/utils/general.py

@@ -35,7 +35,17 @@ import pkg_resources as pkg
 import torch
 import torchvision
 import yaml
-from ultralytics.yolo.utils.checks import check_requirements
+
+# Import 'ultralytics' package or install if if missing
+try:
+    import ultralytics
+
+    assert hasattr(ultralytics, '__version__')  # verify package is not directory
+except (ImportError, AssertionError):
+    os.system('pip install -U ultralytics')
+    import ultralytics
+
+from ultralytics.utils.checks import check_requirements
 
 from utils import TryExcept, emojis
 from utils.downloads import curl_download, gsutil_getsize
@@ -371,7 +381,7 @@ def check_git_info(path='.'):
         return {'remote': None, 'branch': None, 'commit': None}
 
 
-def check_python(minimum='3.7.0'):
+def check_python(minimum='3.8.0'):
     # Check current python version vs. required python version
     check_version(platform.python_version(), minimum, name='Python ', hard=True)
 

yolov5/utils/google_app_engine/additional_requirements.txt

@@ -1,5 +1,5 @@
 # add these requirements in your app on top of the existing ones
-pip==21.1
+pip==23.3
 Flask==2.3.2
 gunicorn==19.10.0
-werkzeug>=2.2.3 # not directly required, pinned by Snyk to avoid a vulnerability
+werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability

yolov5/utils/loggers/clearml/clearml_utils.py

@@ -5,8 +5,7 @@ from pathlib import Path
 
 import numpy as np
 import yaml
-
-from utils.plots import Annotator, colors
+from ultralytics.utils.plotting import Annotator, colors
 
 try:
     import clearml

yolov5/utils/loggers/comet/README.md

@@ -59,7 +59,7 @@ Check out an example of a [completed run here](https://www.comet.com/examples/co
 
 Or better yet, try it out yourself in this Colab Notebook
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb)
 
 # Log automatically
 

yolov5/utils/loggers/comet/__init__.py

@@ -42,7 +42,7 @@ COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
 COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true'
 
 # Evaluation Settings
-COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true'
+COMET_LOG_CONFUSION_MATRIX = (os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true')
 COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true'
 COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100))
 
@@ -51,10 +51,10 @@ CONF_THRES = float(os.getenv('CONF_THRES', 0.001))
 IOU_THRES = float(os.getenv('IOU_THRES', 0.6))
 
 # Batch Logging Settings
-COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true'
+COMET_LOG_BATCH_METRICS = (os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true')
 COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1)
 COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1)
-COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true'
+COMET_LOG_PER_CLASS_METRICS = (os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true')
 
 RANK = int(os.getenv('RANK', -1))
 
@@ -137,7 +137,7 @@ class CometLogger:
 
         self.comet_log_predictions = COMET_LOG_PREDICTIONS
         if self.opt.bbox_interval == -1:
-            self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
+            self.comet_log_prediction_interval = (1 if self.opt.epochs < 10 else self.opt.epochs // 10)
         else:
             self.comet_log_prediction_interval = self.opt.bbox_interval
 
@@ -232,7 +232,8 @@ class CometLogger:
         with open(data_file) as f:
             data_config = yaml.safe_load(f)
 
-        if data_config['path'].startswith(COMET_PREFIX):
+        path = data_config.get('path')
+        if path and path.startswith(COMET_PREFIX):
             path = data_config['path'].replace(COMET_PREFIX, '')
             data_dict = self.download_dataset_artifact(path)
 
@@ -313,8 +314,16 @@ class CometLogger:
             image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
 
             try:
-                artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split})
-                artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split})
+                artifact.add(
+                    image_file,
+                    logical_path=image_logical_path,
+                    metadata={'split': split},
+                )
+                artifact.add(
+                    label_file,
+                    logical_path=label_logical_path,
+                    metadata={'split': split},
+                )
             except ValueError as e:
                 logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
                 logger.error(f'COMET ERROR: {e}')
@@ -356,15 +365,14 @@ class CometLogger:
         data_dict['path'] = artifact_save_dir
 
         metadata_names = metadata.get('names')
-        if type(metadata_names) == dict:
+        if isinstance(metadata_names, dict):
             data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()}
-        elif type(metadata_names) == list:
+        elif isinstance(metadata_names, list):
             data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
         else:
             raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
 
-        data_dict = self.update_data_paths(data_dict)
-        return data_dict
+        return self.update_data_paths(data_dict)
 
     def update_data_paths(self, data_dict):
         path = data_dict.get('path', '')
@@ -476,8 +484,9 @@ class CometLogger:
                             'f1': f1[i],
                             'true_positives': tp[i],
                             'false_positives': fp[i],
-                            'support': nt[c]},
-                        prefix=class_name)
+                            'support': nt[c], },
+                        prefix=class_name,
+                    )
 
         if self.comet_log_confusion_matrix:
             epoch = self.experiment.curr_epoch

yolov5/utils/plots.py

@@ -8,7 +8,6 @@ import math
 import os
 from copy import copy
 from pathlib import Path
-from urllib.error import URLError
 
 import cv2
 import matplotlib
@@ -17,14 +16,13 @@ import numpy as np
 import pandas as pd
 import seaborn as sn
 import torch
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image, ImageDraw
 from scipy.ndimage.filters import gaussian_filter1d
+from ultralytics.utils.plotting import Annotator
 
 from utils import TryExcept, threaded
-from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
-                           is_ascii, xywh2xyxy, xyxy2xywh)
+from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh
 from utils.metrics import fitness
-from utils.segment.general import scale_image
 
 # Settings
 RANK = int(os.getenv('RANK', -1))
@@ -53,120 +51,6 @@ class Colors:
 colors = Colors()  # create instance for 'from utils.plots import colors'
 
 
-def check_pil_font(font=FONT, size=10):
-    # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
-    font = Path(font)
-    font = font if font.exists() else (CONFIG_DIR / font.name)
-    try:
-        return ImageFont.truetype(str(font) if font.exists() else font.name, size)
-    except Exception:  # download if missing
-        try:
-            check_font(font)
-            return ImageFont.truetype(str(font), size)
-        except TypeError:
-            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374
-        except URLError:  # not online
-            return ImageFont.load_default()
-
-
-class Annotator:
-    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
-    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
-        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
-        non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic
-        self.pil = pil or non_ascii
-        if self.pil:  # use PIL
-            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
-            self.draw = ImageDraw.Draw(self.im)
-            self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
-                                       size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
-        else:  # use cv2
-            self.im = im
-        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
-
-    def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
-        # Add one xyxy box to image with label
-        if self.pil or not is_ascii(label):
-            self.draw.rectangle(box, width=self.lw, outline=color)  # box
-            if label:
-                w, h = self.font.getsize(label)  # text width, height (WARNING: deprecated) in 9.2.0
-                # _, _, w, h = self.font.getbbox(label)  # text width, height (New)
-                outside = box[1] - h >= 0  # label fits outside box
-                self.draw.rectangle(
-                    (box[0], box[1] - h if outside else box[1], box[0] + w + 1,
-                     box[1] + 1 if outside else box[1] + h + 1),
-                    fill=color,
-                )
-                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
-                self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
-        else:  # cv2
-            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
-            if label:
-                tf = max(self.lw - 1, 1)  # font thickness
-                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
-                outside = p1[1] - h >= 3
-                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
-                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
-                cv2.putText(self.im,
-                            label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
-                            0,
-                            self.lw / 3,
-                            txt_color,
-                            thickness=tf,
-                            lineType=cv2.LINE_AA)
-
-    def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
-        """Plot masks at once.
-        Args:
-            masks (tensor): predicted masks on cuda, shape: [n, h, w]
-            colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
-            im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
-            alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
-        """
-        if self.pil:
-            # convert to numpy first
-            self.im = np.asarray(self.im).copy()
-        if len(masks) == 0:
-            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
-        colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
-        colors = colors[:, None, None]  # shape(n,1,1,3)
-        masks = masks.unsqueeze(3)  # shape(n,h,w,1)
-        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)
-
-        inv_alph_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
-        mcs = (masks_color * inv_alph_masks).sum(0) * 2  # mask color summand shape(n,h,w,3)
-
-        im_gpu = im_gpu.flip(dims=[0])  # flip channel
-        im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
-        im_gpu = im_gpu * inv_alph_masks[-1] + mcs
-        im_mask = (im_gpu * 255).byte().cpu().numpy()
-        self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)
-        if self.pil:
-            # convert im back to PIL and update draw
-            self.fromarray(self.im)
-
-    def rectangle(self, xy, fill=None, outline=None, width=1):
-        # Add rectangle to image (PIL-only)
-        self.draw.rectangle(xy, fill, outline, width)
-
-    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
-        # Add text to image (PIL-only)
-        if anchor == 'bottom':  # start y from font bottom
-            w, h = self.font.getsize(text)  # text width, height
-            xy[1] += 1 - h
-        self.draw.text(xy, text, fill=txt_color, font=self.font)
-
-    def fromarray(self, im):
-        # Update self.im from a numpy array
-        self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
-        self.draw = ImageDraw.Draw(self.im)
-
-    def result(self):
-        # Return annotated image as array
-        return np.asarray(self.im)
-
-
 def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
     """
     x:              Features to be visualized
@@ -266,7 +150,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
         if paths:
-            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
+            annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
             ti = targets[targets[:, 0] == i]  # image targets
             boxes = xywh2xyxy(ti[:, 2:6]).T

yolov5/utils/segment/plots.py

@@ -54,7 +54,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
         if paths:
-            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
+            annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
             idx = targets[:, 0] == i
             ti = targets[idx]  # image targets

yolov5/val.py

@@ -304,6 +304,8 @@ def run(
     if save_json and len(jdict):
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
         anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json'))  # annotations
+        if not os.path.exists(anno_json):
+            anno_json = os.path.join(data['path'], 'annotations', 'instances_val2017.json')
         pred_json = str(save_dir / f'{w}_predictions.json')  # predictions
         LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
         with open(pred_json, 'w') as f: