Merge branch 'main' into quan

.github/workflows/docker.yaml

@@ -54,7 +54,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-      max-parallel: 6
+      max-parallel: 10
       matrix:
         include:
           - dockerfile: "Dockerfile"
@@ -82,6 +82,15 @@ jobs:
           #   tags: "latest-conda"
           #   platforms: "linux/amd64"
     steps:
+      - name: Cleanup toolcache
+        # Free up to 10GB of disk space per https://github.com/ultralytics/ultralytics/pull/14894
+        run: |
+          echo "Free space before deletion:"
+          df -h /
+          rm -rf /opt/hostedtoolcache
+          echo "Free space after deletion:"
+          df -h /
+
       - name: Checkout repo
         uses: actions/checkout@v4
         with:
@@ -133,7 +142,7 @@ jobs:
         uses: nick-invision/retry@v3
         with:
           timeout_minutes: 120
-          retry_wait_seconds: 30
+          retry_wait_seconds: 60
           max_attempts: 2 # retry once
           command: |
             docker build \

.gitignore

@@ -26,6 +26,9 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
+requirements.txt
+setup.py
+ultralytics.egg-info
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

CONTRIBUTING.md

@@ -18,10 +18,11 @@ Thank you for your interest in contributing to Ultralytics open-source YOLO repo
 3. [Reporting Bugs](#reporting-bugs)
 4. [License](#license)
 5. [Conclusion](#conclusion)
+6. [FAQ](#faq)
 
 ## Code of Conduct
 
-All contributors must adhere to the [Code of Conduct](code_of_conduct.md) to ensure a welcoming and inclusive environment for everyone.
+All contributors must adhere to the [Code of Conduct](https://docs.ultralytics.com/help/code_of_conduct) to ensure a welcoming and inclusive environment for everyone.
 
 ## Contributing via Pull Requests
 
@@ -41,7 +42,7 @@ We welcome contributions in the form of pull requests. To streamline the review
 
 ### CLA Signing
 
-Before we can accept your pull request, you must sign a [Contributor License Agreement (CLA)](CLA.md). This legal document ensures that your contributions are properly licensed and that the project can continue to be distributed under the AGPL-3.0 license.
+Before we can accept your pull request, you must sign a [Contributor License Agreement (CLA)](https://docs.ultralytics.com/help/CLA). This legal document ensures that your contributions are properly licensed and that the project can continue to be distributed under the AGPL-3.0 license.
 
 To sign the CLA, follow the instructions provided by the CLA bot after you submit your PR and add a comment in your PR saying:
 
@@ -55,13 +56,12 @@ When adding new functions or classes, include a [Google-style docstring](https:/
 
 #### Google-style
 
-This example shows a Google-style docstring. Note that both input and output `types` must always be enclosed by parentheses, i.e. `(bool)`.
+This example shows a Google-style docstring. Note that both input and output `types` must always be enclosed by parentheses, i.e., `(bool)`.
 
 ```python
 def example_function(arg1, arg2=4):
     """
-    This example shows a Google-style docstring. Note that both input and output `types` must always be enclosed by
-    parentheses, i.e., `(bool)`.
+    Example function that demonstrates Google-style docstrings.
 
     Args:
         arg1 (int): The first argument.
@@ -80,13 +80,12 @@ def example_function(arg1, arg2=4):
 
 #### Google-style with type hints
 
-This example shows both a Google-style docstring and argument and return type hints, though both are not required, one can be used without the other.
+This example shows both a Google-style docstring and argument and return type hints, though both are not required; one can be used without the other.
 
 ```python
 def example_function(arg1: int, arg2: int = 4) -> bool:
     """
-    This example shows both a Google-style docstring and argument and return type hints, though both are not required;
-    one can be used without the other.
+    Example function that demonstrates Google-style docstrings.
 
     Args:
         arg1: The first argument.
@@ -105,7 +104,7 @@ def example_function(arg1: int, arg2: int = 4) -> bool:
 
 #### Single-line
 
-Smaller or simpler functions can utilize a single-line docstring. Note the docstring must use 3 double-quotes, and be a complete sentence starting with a capital letter and ending with a period.
+Smaller or simpler functions can utilize a single-line docstring. Note the docstring must use 3 double-quotes and be a complete sentence starting with a capital letter and ending with a period.
 
 ```python
 def example_small_function(arg1: int, arg2: int = 4) -> bool:
@@ -115,11 +114,11 @@ def example_small_function(arg1: int, arg2: int = 4) -> bool:
 
 ### GitHub Actions CI Tests
 
-Before your pull request can be merged, all GitHub Actions [Continuous Integration](https://docs.ultralytics.com/help/CI/) (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure that your changes meet the quality standards of the project. Make sure to review the output of the GitHub Actions and fix any issues
+Before your pull request can be merged, all GitHub Actions [Continuous Integration](https://docs.ultralytics.com/help/CI) (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure your changes meet the project's quality standards. Review the output of the GitHub Actions and fix any issues.
 
 ## Reporting Bugs
 
-We appreciate bug reports as they play a crucial role in maintaining the project's quality. When reporting bugs it is important to provide a [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum_reproducible_example/): a clear, concise code example that replicates the issue. This helps in quick identification and resolution of the bug.
+We appreciate bug reports as they play a crucial role in maintaining the project's quality. When reporting bugs, it is important to provide a [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum_reproducible_example): a clear, concise code example that replicates the issue. This helps in quick identification and resolution of the bug.
 
 ## License
 
@@ -132,3 +131,31 @@ Users and developers are encouraged to familiarize themselves with the terms of
 Thank you for your interest in contributing to [Ultralytics open-source](https://github.com/ultralytics) YOLO projects. Your participation is crucial in shaping the future of our software and fostering a community of innovation and collaboration. Whether you're improving code, reporting bugs, or suggesting features, your contributions make a significant impact.
 
 We look forward to seeing your ideas in action and appreciate your commitment to advancing object detection technology. Let's continue to grow and innovate together in this exciting open-source journey. Happy coding! 🚀🌟
+
+## FAQ
+
+### Why should I contribute to Ultralytics YOLO open-source repositories?
+
+Contributing to Ultralytics YOLO open-source repositories helps improve the software, making it more robust and feature-rich for the entire community. Contributions can include code enhancements, bug fixes, documentation improvements, and new feature implementations. Additionally, contributing offers the chance to collaborate with other skilled developers and experts in the field, boosting your own skills and reputation. For information on how to get started, refer to the [Contributing via Pull Requests](#contributing-via-pull-requests) section.
+
+### How do I sign the Contributor License Agreement (CLA) for Ultralytics YOLO?
+
+To sign the Contributor License Agreement (CLA), follow the instructions provided by the CLA bot after submitting your pull request. This will ensure your contributions are properly licensed under the AGPL-3.0 license, maintaining the legal integrity of the open-source project. Add a comment in your pull request mentioning:
+
+```
+I have read the CLA Document and I sign the CLA
+```
+
+For more information, see the [CLA Signing](#cla-signing) section.
+
+### What are Google-style docstrings and why are they required for Ultralytics YOLO contributions?
+
+Google-style docstrings provide clear and concise documentation for functions and classes, enhancing readability and maintainability of the code. These docstrings outline the function's purpose, arguments, and return values with specific formatting rules. When contributing to Ultralytics YOLO, adhering to Google-style docstrings ensures that your additions are comprehensible and well-documented. For examples and guidelines, visit the [Google-Style Docstrings](#google-style-docstrings) section.
+
+### How can I ensure my changes pass the GitHub Actions CI tests?
+
+Before your pull request is merged, it must pass all GitHub Actions Continuous Integration (CI) tests. These tests include linting, unit tests, and other checks to ensure the code meets the project's quality standards. Review the output of the GitHub Actions and address any issues. For detailed information on the CI process and troubleshooting tips, see the [GitHub Actions CI Tests](#github-actions-ci-tests) section.
+
+### How do I report a bug in Ultralytics YOLO repositories?
+
+To report a bug, provide a clear and concise [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum_reproducible_example) along with your bug report. This helps developers quickly identify and fix the issue. Ensure your example is minimal yet sufficient to replicate the problem. For more detailed steps on reporting bugs, refer to the [Reporting Bugs](#reporting-bugs) section.

docs/en/datasets/classify/caltech101.md

@@ -46,7 +46,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
         ```
 
 ## Sample Images and Annotations
@@ -108,7 +108,7 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
         ```
 For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.
 

docs/en/datasets/classify/caltech256.md

@@ -57,7 +57,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
         ```
 
 ## Sample Images and Annotations
@@ -116,7 +116,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
+        yolo classify train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
         ```
 
 ### What are the most common use cases for the Caltech-256 dataset?

docs/en/datasets/classify/cifar10.md

@@ -60,7 +60,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
         ```
 
 ## Sample Images and Annotations
@@ -114,7 +114,7 @@ To train a YOLO model on the CIFAR-10 dataset using Ultralytics, you can follow
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
         ```
 
 For more details, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/cifar100.md

@@ -49,7 +49,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
         ```
 
 ## Sample Images and Annotations
@@ -107,7 +107,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
         
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
+        yolo classify train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
         ```
 
 For a comprehensive list of available arguments, please refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/classify/fashion-mnist.md

@@ -74,7 +74,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 epochs with an image s
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
         ```
 
 ## Sample Images and Annotations
@@ -117,7 +117,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
     === "CLI"
     
         ```bash
-        yolo detect train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
         ```
 
 For more detailed training parameters, refer to the [Training page](../../modes/train.md).

docs/en/datasets/classify/imagenet.md

@@ -59,7 +59,7 @@ To train a deep learning model on the ImageNet dataset for 100 epochs with an im
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -120,7 +120,7 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
     
         ```bash
         # Start training from a pretrained *.pt model
-        yolo train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
         ```
 
 For more in-depth training instruction, refer to our [Training page](../../modes/train.md).

docs/en/datasets/classify/imagenet10.md

@@ -45,7 +45,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
+        yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -104,7 +104,7 @@ To test your deep learning model on the ImageNet10 dataset with an image size of
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
+        yolo classify train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
         ```
 
 Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.

docs/en/datasets/classify/imagenette.md

@@ -47,7 +47,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Sample Images and Annotations
@@ -82,7 +82,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette160
-        yolo detect train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
+        yolo classify train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
         ```
 
 !!! Example "Train Example with ImageNette320"
@@ -103,7 +103,7 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
 
         ```bash
         # Start training from a pretrained *.pt model with ImageNette320
-        yolo detect train data=imagenette320 model=yolov8n-cls.pt epochs=100 imgsz=320
+        yolo classify train data=imagenette320 model=yolov8n-cls.pt epochs=100 imgsz=320
         ```
 
 These smaller versions of the dataset allow for rapid iterations during the development process while still providing valuable and realistic image classification tasks.
@@ -140,7 +140,7 @@ To train a YOLO model on the ImageNette dataset for 100 epochs, you can use the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
         ```
 
 For more details, see the [Training](../../modes/train.md) documentation page.

docs/en/datasets/classify/imagewoof.md

@@ -44,7 +44,7 @@ To train a CNN model on the ImageWoof dataset for 100 epochs with an image size
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
         ```
 
 ## Dataset Variants
@@ -113,7 +113,7 @@ To train a Convolutional Neural Network (CNN) model on the ImageWoof dataset usi
     === "CLI"
     
         ```bash
-        yolo detect train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
+        yolo classify train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
         ```
 
 For more details on available training arguments, refer to the [Training](../../modes/train.md) page.

docs/en/datasets/classify/mnist.md

@@ -52,7 +52,7 @@ To train a CNN model on the MNIST dataset for 100 epochs with an image size of 3
 
         ```bash
         # Start training from a pretrained *.pt model
-        cnn detect train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
         ```
 
 ## Sample Images and Annotations
@@ -113,7 +113,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
     
         ```bash
         # Start training from a pretrained *.pt model
-        cnn detect train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        yolo classify train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
         ```
 
 For a detailed list of available training arguments, refer to the [Training](../../modes/train.md) page.

docs/en/datasets/obb/index.md

@@ -50,7 +50,7 @@ To train a model using these OBB formats:
 
         ```bash
         # Train a new YOLOv8n-OBB model on the DOTAv2 dataset
-        yolo detect train data=DOTAv1.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets
@@ -125,7 +125,7 @@ Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO
     
         ```bash
         # Train a new YOLOv8n-OBB model on the custom dataset
-        yolo detect train data=your_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo obb train data=your_dataset.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
         ```
         
 This ensures your model leverages the detailed OBB annotations for improved detection accuracy.

docs/en/datasets/pose/coco.md

@@ -71,7 +71,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an im
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -133,7 +133,7 @@ Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using eithe
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo pose train data=coco-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
         ```
 
 For more details on the training process and available arguments, check the [training page](../../modes/train.md).

docs/en/datasets/pose/coco8-pose.md

@@ -44,7 +44,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -105,7 +105,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
     === "CLI"
     
         ```bash
-        yolo detect train data=coco8-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
         ```
 
 For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.

docs/en/datasets/pose/index.md

@@ -82,7 +82,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets

docs/en/datasets/pose/tiger-pose.md

@@ -126,7 +126,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
     
         ```bash
         # Start training from a pretrained *.pt model
-        yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
         ```
 
 ### What configurations does the `tiger-pose.yaml` file include?

docs/en/datasets/segment/coco.md

@@ -69,7 +69,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -131,7 +131,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
     
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco-seg.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo segment train data=coco-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
         ```
 
 ### What are the key features of the COCO-Seg dataset?

docs/en/datasets/segment/coco8-seg.md

@@ -44,7 +44,7 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an ima
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -106,7 +106,7 @@ To train a **YOLOv8n-seg** model on the COCO8-Seg dataset for 100 epochs with an
     
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8-seg.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
         ```
 
 For a thorough explanation of available arguments and configuration options, you can check the [Training](../../modes/train.md) documentation.

docs/en/datasets/segment/index.md

@@ -84,7 +84,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo detect train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Supported Datasets

docs/en/guides/ros-quickstart.md

@@ -1,6 +1,6 @@
 ---
 comments: true
-description: todo
+description: Learn to integrate Ultralytics YOLO with your robot running ROS Noetic, utilizing RGB images, depth images, and point clouds for efficient object detection, segmentation, and enhanced robotic perception.
 keywords: Ultralytics, YOLO, object detection, deep learning, machine learning, guide, ROS, Robot Operating System, robotics, ROS Noetic, Python, Ubuntu, simulation, visualization, communication, middleware, hardware abstraction, tools, utilities, ecosystem, Noetic Ninjemys, autonomous vehicle, AMV
 ---
 

docs/en/help/contributing.md

@@ -18,10 +18,11 @@ Thank you for your interest in contributing to Ultralytics open-source YOLO repo
 3. [Reporting Bugs](#reporting-bugs)
 4. [License](#license)
 5. [Conclusion](#conclusion)
+6. [FAQ](#faq)
 
 ## Code of Conduct
 
-All contributors must adhere to the [Code of Conduct](code_of_conduct.md) to ensure a welcoming and inclusive environment for everyone.
+All contributors must adhere to the [Code of Conduct](https://docs.ultralytics.com/help/code_of_conduct) to ensure a welcoming and inclusive environment for everyone.
 
 ## Contributing via Pull Requests
 
@@ -41,7 +42,7 @@ We welcome contributions in the form of pull requests. To streamline the review
 
 ### CLA Signing
 
-Before we can accept your pull request, you must sign a [Contributor License Agreement (CLA)](CLA.md). This legal document ensures that your contributions are properly licensed and that the project can continue to be distributed under the AGPL-3.0 license.
+Before we can accept your pull request, you must sign a [Contributor License Agreement (CLA)](https://docs.ultralytics.com/help/CLA). This legal document ensures that your contributions are properly licensed and that the project can continue to be distributed under the AGPL-3.0 license.
 
 To sign the CLA, follow the instructions provided by the CLA bot after you submit your PR and add a comment in your PR saying:
 
@@ -58,6 +59,7 @@ When adding new functions or classes, include a [Google-style docstring](https:/
     === "Google-style"
 
          This example shows a Google-style docstring. Note that both input and output `types` must always be enclosed by parentheses, i.e., `(bool)`.
+
          ```python
          def example_function(arg1, arg2=4):
              """
@@ -81,6 +83,7 @@ When adding new functions or classes, include a [Google-style docstring](https:/
     === "Google-style with type hints"
 
          This example shows both a Google-style docstring and argument and return type hints, though both are not required; one can be used without the other.
+
          ```python
          def example_function(arg1: int, arg2: int = 4) -> bool:
              """
@@ -104,6 +107,7 @@ When adding new functions or classes, include a [Google-style docstring](https:/
     === "Single-line"
 
          Smaller or simpler functions can utilize a single-line docstring. Note the docstring must use 3 double-quotes and be a complete sentence starting with a capital letter and ending with a period.
+
          ```python
          def example_small_function(arg1: int, arg2: int = 4) -> bool:
              """Example function that demonstrates a single-line docstring."""
@@ -112,11 +116,11 @@ When adding new functions or classes, include a [Google-style docstring](https:/
 
 ### GitHub Actions CI Tests
 
-Before your pull request can be merged, all GitHub Actions [Continuous Integration](CI.md) (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure your changes meet the project's quality standards. Review the output of the GitHub Actions and fix any issues.
+Before your pull request can be merged, all GitHub Actions [Continuous Integration](https://docs.ultralytics.com/help/CI) (CI) tests must pass. These tests include linting, unit tests, and other checks to ensure your changes meet the project's quality standards. Review the output of the GitHub Actions and fix any issues.
 
 ## Reporting Bugs
 
-We appreciate bug reports as they play a crucial role in maintaining the project's quality. When reporting bugs, it is important to provide a [Minimum Reproducible Example](minimum_reproducible_example.md): a clear, concise code example that replicates the issue. This helps in quick identification and resolution of the bug.
+We appreciate bug reports as they play a crucial role in maintaining the project's quality. When reporting bugs, it is important to provide a [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum_reproducible_example): a clear, concise code example that replicates the issue. This helps in quick identification and resolution of the bug.
 
 ## License
 
@@ -156,4 +160,4 @@ Before your pull request is merged, it must pass all GitHub Actions Continuous I
 
 ### How do I report a bug in Ultralytics YOLO repositories?
 
-To report a bug, provide a clear and concise [Minimum Reproducible Example](../help/minimum_reproducible_example.md) along with your bug report. This helps developers quickly identify and fix the issue. Ensure your example is minimal yet sufficient to replicate the problem. For more detailed steps on reporting bugs, refer to the [Reporting Bugs](#reporting-bugs) section.
+To report a bug, provide a clear and concise [Minimum Reproducible Example](https://docs.ultralytics.com/help/minimum_reproducible_example) along with your bug report. This helps developers quickly identify and fix the issue. Ensure your example is minimal yet sufficient to replicate the problem. For more detailed steps on reporting bugs, refer to the [Reporting Bugs](#reporting-bugs) section.

docs/en/models/sam-2.md

@@ -6,10 +6,6 @@ keywords: SAM 2, Segment Anything, video segmentation, image segmentation, promp
 
 # SAM 2: Segment Anything Model 2
 
-!!! Note "🚧 SAM 2 Integration In Progress 🚧"
-
-    The SAM 2 features described in this documentation are currently not enabled in the `ultralytics` package. The Ultralytics team is actively working on integrating SAM 2, and these capabilities should be available soon. We appreciate your patience as we work to implement this exciting new model.
-
 SAM 2, the successor to Meta's [Segment Anything Model (SAM)](sam.md), is a cutting-edge tool designed for comprehensive object segmentation in both images and videos. It excels in handling complex visual data through a unified, promptable model architecture that supports real-time processing and zero-shot generalization.
 
 ![SAM 2 Example Results](https://github.com/facebookresearch/segment-anything-2/raw/main/assets/sa_v_dataset.jpg?raw=true)
@@ -105,10 +101,6 @@ pip install ultralytics
 
 ## How to Use SAM 2: Versatility in Image and Video Segmentation
 
-!!! Note "🚧 SAM 2 Integration In Progress 🚧"
-
-    The SAM 2 features described in this documentation are currently not enabled in the `ultralytics` package. The Ultralytics team is actively working on integrating SAM 2, and these capabilities should be available soon. We appreciate your patience as we work to implement this exciting new model.
-
 The following table details the available SAM 2 models, their pre-trained weights, supported tasks, and compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md).
 
 | Model Type  | Pre-trained Weights                                                                   | Tasks Supported                              | Inference | Validation | Training | Export |

docs/en/quickstart.md

@@ -162,8 +162,8 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
         yolo TASK MODE ARGS
         ```
 
-        - `TASK` (optional) is one of ([detect](tasks/detect.md), [segment](tasks/segment.md), [classify](tasks/classify.md), [pose](tasks/pose.md))
-        - `MODE` (required) is one of ([train](modes/train.md), [val](modes/val.md), [predict](modes/predict.md), [export](modes/export.md), [track](modes/track.md))
+        - `TASK` (optional) is one of ([detect](tasks/detect.md), [segment](tasks/segment.md), [classify](tasks/classify.md), [pose](tasks/pose.md), [obb](tasks/obb.md))
+        - `MODE` (required) is one of ([train](modes/train.md), [val](modes/val.md), [predict](modes/predict.md), [export](modes/export.md), [track](modes/track.md), [benchmark](modes/benchmark.md))
         - `ARGS` (optional) are `arg=value` pairs like `imgsz=640` that override defaults.
 
         See all `ARGS` in the full [Configuration Guide](usage/cfg.md) or with the `yolo cfg` CLI command.

docs/en/reference/data/converter.md

@@ -23,6 +23,10 @@ keywords: Ultralytics, data conversion, YOLO models, COCO, DOTA, YOLO bbox2segme
 
 <br><br><hr><br>
 
+## ::: ultralytics.data.converter.convert_segment_masks_to_yolo_seg
+
+<br><br><hr><br>
+
 ## ::: ultralytics.data.converter.convert_dota_to_yolo_obb
 
 <br><br><hr><br>

docs/en/usage/cli.md

@@ -27,8 +27,8 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
         ```bash
         yolo TASK MODE ARGS
 
-        Where   TASK (optional) is one of [detect, segment, classify]
-                MODE (required) is one of [train, val, predict, export, track]
+        Where   TASK (optional) is one of [detect, segment, classify, pose, obb]
+                MODE (required) is one of [train, val, predict, export, track, benchmark]
                 ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
         ```
         See all ARGS in the full [Configuration Guide](cfg.md) or with `yolo cfg`
@@ -75,8 +75,8 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
 
 Where:
 
-- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess the `TASK` from the model type.
-- `MODE` (required) is one of `[train, val, predict, export, track]`
+- `TASK` (optional) is one of `[detect, segment, classify, pose, obb]`. If it is not passed explicitly YOLOv8 will try to guess the `TASK` from the model type.
+- `MODE` (required) is one of `[train, val, predict, export, track, benchmark]`
 - `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults. For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
 
 !!! Warning "Warning"

docs/en/usage/simple-utilities.md

@@ -51,6 +51,22 @@ auto_annotate(  # (1)!
 
 - Use in combination with the [function `segments2boxes`](#convert-segments-to-bounding-boxes) to generate object detection bounding boxes as well
 
+### Convert Segmentation Masks into YOLO Format
+
+![Segmentation Masks to YOLO Format](https://github.com/user-attachments/assets/1a823fc1-f3a1-4dd5-83e7-0b209df06fc3)
+
+Use to convert a dataset of segmentation mask images to the `YOLO` segmentation format.
+This function takes the directory containing the binary format mask images and converts them into YOLO segmentation format.
+
+The converted masks will be saved in the specified output directory.
+
+```python
+from ultralytics.data.converter import convert_segment_masks_to_yolo_seg
+
+# For COCO dataset we have 80 classes
+convert_segment_masks_to_yolo_seg(masks_dir="path/to/masks_dir", output_dir="path/to/output_dir", classes=80)
+```
+
 ### Convert COCO into YOLO Format
 
 Use to convert COCO JSON annotations into proper YOLO format. For object detection (bounding box) datasets, `use_segments` and `use_keypoints` should both be `False`

docs/mkdocs_github_authors.yaml

@@ -1,3 +1,4 @@
+116908874+jk4e@users.noreply.github.com: jk4e
 1185102784@qq.com: Laughing-q
 130829914+IvorZhu331@users.noreply.github.com: IvorZhu331
 135830346+UltralyticsAssistant@users.noreply.github.com: UltralyticsAssistant

tests/test_cli.py

@@ -8,6 +8,7 @@ from PIL import Image
 from tests import CUDA_DEVICE_COUNT, CUDA_IS_AVAILABLE
 from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
 from ultralytics.utils import ASSETS, WEIGHTS_DIR, checks
+from ultralytics.utils.torch_utils import TORCH_1_9
 
 # Constants
 TASK_MODEL_DATA = [(task, WEIGHTS_DIR / TASK2MODEL[task], TASK2DATA[task]) for task in TASKS]
@@ -57,6 +58,8 @@ def test_rtdetr(task="detect", model="yolov8n-rtdetr.yaml", data="coco8.yaml"):
     # Warning: must use imgsz=640 (note also add coma, spaces, fraction=0.25 args to test single-image training)
     run(f"yolo train {task} model={model} data={data} --imgsz= 160 epochs =1, cache = disk fraction=0.25")
     run(f"yolo predict {task} model={model} source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt")
+    if TORCH_1_9:
+        run(f"yolo predict {task} model='rtdetr-l.pt' source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt")
 
 
 @pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="MobileSAM with CLIP is not supported in Python 3.12")

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.70"
+__version__ = "8.2.72"
 
 import os
 

ultralytics/data/converter.py

@@ -334,6 +334,87 @@ def convert_coco(
     LOGGER.info(f"{'LVIS' if lvis else 'COCO'} data converted successfully.\nResults saved to {save_dir.resolve()}")
 
 
+def convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes):
+    """
+    Converts a dataset of segmentation mask images to the YOLO segmentation format.
+
+    This function takes the directory containing the binary format mask images and converts them into YOLO segmentation format.
+    The converted masks are saved in the specified output directory.
+
+    Args:
+        masks_dir (str): The path to the directory where all mask images (png, jpg) are stored.
+        output_dir (str): The path to the directory where the converted YOLO segmentation masks will be stored.
+        classes (int): Total classes in the dataset i.e for COCO classes=80
+
+    Example:
+        ```python
+        from ultralytics.data.converter import convert_segment_masks_to_yolo_seg
+
+        # for coco dataset, we have 80 classes
+        convert_segment_masks_to_yolo_seg('path/to/masks_directory', 'path/to/output/directory', classes=80)
+        ```
+
+    Notes:
+        The expected directory structure for the masks is:
+
+            - masks
+                ├─ mask_image_01.png or mask_image_01.jpg
+                ├─ mask_image_02.png or mask_image_02.jpg
+                ├─ mask_image_03.png or mask_image_03.jpg
+                └─ mask_image_04.png or mask_image_04.jpg
+
+        After execution, the labels will be organized in the following structure:
+
+            - output_dir
+                ├─ mask_yolo_01.txt
+                ├─ mask_yolo_02.txt
+                ├─ mask_yolo_03.txt
+                └─ mask_yolo_04.txt
+    """
+    import os
+
+    pixel_to_class_mapping = {i + 1: i for i in range(80)}
+    for mask_filename in os.listdir(masks_dir):
+        if mask_filename.endswith(".png"):
+            mask_path = os.path.join(masks_dir, mask_filename)
+            mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)  # Read the mask image in grayscale
+            img_height, img_width = mask.shape  # Get image dimensions
+            LOGGER.info(f"Processing {mask_path} imgsz = {img_height} x {img_width}")
+
+            unique_values = np.unique(mask)  # Get unique pixel values representing different classes
+            yolo_format_data = []
+
+            for value in unique_values:
+                if value == 0:
+                    continue  # Skip background
+                class_index = pixel_to_class_mapping.get(value, -1)
+                if class_index == -1:
+                    LOGGER.warning(f"Unknown class for pixel value {value} in file {mask_filename}, skipping.")
+                    continue
+
+                # Create a binary mask for the current class and find contours
+                contours, _ = cv2.findContours(
+                    (mask == value).astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+                )  # Find contours
+
+                for contour in contours:
+                    if len(contour) >= 3:  # YOLO requires at least 3 points for a valid segmentation
+                        contour = contour.squeeze()  # Remove single-dimensional entries
+                        yolo_format = [class_index]
+                        for point in contour:
+                            # Normalize the coordinates
+                            yolo_format.append(round(point[0] / img_width, 6))  # Rounding to 6 decimal places
+                            yolo_format.append(round(point[1] / img_height, 6))
+                        yolo_format_data.append(yolo_format)
+            # Save Ultralytics YOLO format data to file
+            output_path = os.path.join(output_dir, os.path.splitext(mask_filename)[0] + ".txt")
+            with open(output_path, "w") as file:
+                for item in yolo_format_data:
+                    line = " ".join(map(str, item))
+                    file.write(line + "\n")
+            LOGGER.info(f"Processed and stored at {output_path} imgsz = {img_height} x {img_width}")
+
+
 def convert_dota_to_yolo_obb(dota_root_path: str):
     """
     Converts DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format.

ultralytics/engine/trainer.py

@@ -26,6 +26,7 @@ from ultralytics.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
 from ultralytics.utils import (
     DEFAULT_CFG,
+    LOCAL_RANK,
     LOGGER,
     RANK,
     TQDM,
@@ -129,7 +130,7 @@ class BaseTrainer:
 
         # Model and Dataset
         self.model = check_model_file_from_stem(self.args.model)  # add suffix, i.e. yolov8n -> yolov8n.pt
-        with torch_distributed_zero_first(RANK):  # avoid auto-downloading dataset multiple times
+        with torch_distributed_zero_first(LOCAL_RANK):  # avoid auto-downloading dataset multiple times
             self.trainset, self.testset = self.get_dataset()
         self.ema = None
 
@@ -285,7 +286,7 @@ class BaseTrainer:
 
         # Dataloaders
         batch_size = self.batch_size // max(world_size, 1)
-        self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode="train")
+        self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=LOCAL_RANK, mode="train")
         if RANK in {-1, 0}:
             # Note: When training DOTA dataset, double batch size could get OOM on images with >2000 objects.
             self.test_loader = self.get_dataloader(

ultralytics/models/sam2/predict.py

@@ -102,28 +102,23 @@ class SAM2Predictor(Predictor):
         if bboxes is not None:
             bboxes = torch.as_tensor(bboxes, dtype=torch.float32, device=self.device)
             bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
-            bboxes *= r
+            bboxes = bboxes.view(-1, 2, 2) * r
+            bbox_labels = torch.tensor([[2, 3]], dtype=torch.int32, device=bboxes.device).expand(len(bboxes), -1)
+            # NOTE: merge "boxes" and "points" into a single "points" input
+            # (where boxes are added at the beginning) to model.sam_prompt_encoder
+            if points is not None:
+                points = torch.cat([bboxes, points], dim=1)
+                labels = torch.cat([bbox_labels, labels], dim=1)
+            else:
+                points, labels = bboxes, bbox_labels
         if masks is not None:
             masks = torch.as_tensor(masks, dtype=torch.float32, device=self.device).unsqueeze(1)
 
         points = (points, labels) if points is not None else None
-        # TODO: Embed prompts
-        # if bboxes is not None:
-        #     box_coords = bboxes.reshape(-1, 2, 2)
-        #     box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=bboxes.device)
-        #     box_labels = box_labels.repeat(bboxes.size(0), 1)
-        #     # we merge "boxes" and "points" into a single "concat_points" input (where
-        #     # boxes are added at the beginning) to sam_prompt_encoder
-        #     if concat_points is not None:
-        #         concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)
-        #         concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)
-        #         concat_points = (concat_coords, concat_labels)
-        #     else:
-        #         concat_points = (box_coords, box_labels)
 
         sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
             points=points,
-            boxes=bboxes,
+            boxes=None,
             masks=masks,
         )
         # Predict masks

ultralytics/nn/modules/transformer.py

@@ -186,8 +186,8 @@ class MLP(nn.Module):
     def forward(self, x):
         """Forward pass for the entire MLP."""
         for i, layer in enumerate(self.layers):
-            x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x.sigmoid() if self.sigmoid else x
+            x = getattr(self, "act", nn.ReLU())(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x.sigmoid() if getattr(self, "sigmoid", False) else x
 
 
 class LayerNorm2d(nn.Module):

ultralytics/utils/__init__.py

@@ -47,7 +47,7 @@ PYTHON_VERSION = platform.python_version()
 TORCH_VERSION = torch.__version__
 TORCHVISION_VERSION = importlib.metadata.version("torchvision")  # faster than importing torchvision
 HELP_MSG = """
-    Usage examples for running YOLOv8:
+    Usage examples for running Ultralytics YOLO:
 
     1. Install the ultralytics package:
 
@@ -58,25 +58,25 @@ HELP_MSG = """
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.yaml')  # build a new model from scratch
+        model = YOLO("yolov8n.yaml")  # build a new model from scratch
         model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Use the model
         results = model.train(data="coco8.yaml", epochs=3)  # train the model
         results = model.val()  # evaluate model performance on the validation set
-        results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image
-        success = model.export(format='onnx')  # export the model to ONNX format
+        results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
+        success = model.export(format="onnx")  # export the model to ONNX format
 
     3. Use the command line interface (CLI):
 
-        YOLOv8 'yolo' CLI commands use the following syntax:
+        Ultralytics 'yolo' CLI commands use the following syntax:
 
             yolo TASK MODE ARGS
 
-            Where   TASK (optional) is one of [detect, segment, classify]
-                    MODE (required) is one of [train, val, predict, export]
-                    ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
-                        See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
+            Where   TASK (optional) is one of [detect, segment, classify, pose, obb]
+                    MODE (required) is one of [train, val, predict, export, benchmark]
+                    ARGS (optional) are any number of custom "arg=value" pairs like "imgsz=320" that override defaults.
+                        See all ARGS at https://docs.ultralytics.com/usage/cfg or with "yolo cfg"
 
         - Train a detection model for 10 epochs with an initial learning_rate of 0.01
             yolo detect train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01

ultralytics/utils/downloads.py

@@ -41,7 +41,7 @@ def is_url(url, check=False):
     Args:
         url (str): The string to be validated as a URL.
         check (bool, optional): If True, performs an additional check to see if the URL exists online.
-            Defaults to True.
+            Defaults to False.
 
     Returns:
         (bool): Returns True for a valid URL. If 'check' is True, also returns True if the URL exists online.
@@ -201,7 +201,7 @@ def check_disk_space(url="https://ultralytics.com/assets/coco8.zip", path=Path.c
     Args:
         url (str, optional): The URL to the file. Defaults to 'https://ultralytics.com/assets/coco8.zip'.
         path (str | Path, optional): The path or drive to check the available free space on.
-        sf (float, optional): Safety factor, the multiplier for the required free space. Defaults to 2.0.
+        sf (float, optional): Safety factor, the multiplier for the required free space. Defaults to 1.5.
         hard (bool, optional): Whether to throw an error or not on insufficient disk space. Defaults to True.
 
     Returns:

ultralytics/utils/ops.py

@@ -528,7 +528,7 @@ def ltwh2xywh(x):
 def xyxyxyxy2xywhr(x):
     """
     Convert batched Oriented Bounding Boxes (OBB) from [xy1, xy2, xy3, xy4] to [xywh, rotation]. Rotation values are
-    expected in degrees from 0 to 90.
+    returned in radians from 0 to pi/2.
 
     Args:
         x (numpy.ndarray | torch.Tensor): Input box corners [xy1, xy2, xy3, xy4] of shape (n, 8).
@@ -551,7 +551,7 @@ def xyxyxyxy2xywhr(x):
 def xywhr2xyxyxyxy(x):
     """
     Convert batched Oriented Bounding Boxes (OBB) from [xywh, rotation] to [xy1, xy2, xy3, xy4]. Rotation values should
-    be in degrees from 0 to 90.
+    be in radians from 0 to pi/2.
 
     Args:
         x (numpy.ndarray | torch.Tensor): Boxes in [cx, cy, w, h, rotation] format of shape (n, 5) or (b, n, 5).

ultralytics/utils/plotting.py

@@ -195,12 +195,12 @@ class Annotator:
 
     def circle_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), margin=2):
         """
-        Draws a label with a background rectangle centered within a given bounding box.
+        Draws a label with a background circle centered within a given bounding box.
 
         Args:
             box (tuple): The bounding box coordinates (x1, y1, x2, y2).
             label (str): The text label to be displayed.
-            color (tuple, optional): The background color of the rectangle (R, G, B).
+            color (tuple, optional): The background color of the rectangle (B, G, R).
             txt_color (tuple, optional): The color of the text (R, G, B).
             margin (int, optional): The margin between the text and the rectangle border.
         """
@@ -242,7 +242,7 @@ class Annotator:
         Args:
             box (tuple): The bounding box coordinates (x1, y1, x2, y2).
             label (str): The text label to be displayed.
-            color (tuple, optional): The background color of the rectangle (R, G, B).
+            color (tuple, optional): The background color of the rectangle (B, G, R).
             txt_color (tuple, optional): The color of the text (R, G, B).
             margin (int, optional): The margin between the text and the rectangle border.
         """
@@ -280,7 +280,7 @@ class Annotator:
         Args:
             box (tuple): The bounding box coordinates (x1, y1, x2, y2).
             label (str): The text label to be displayed.
-            color (tuple, optional): The background color of the rectangle (R, G, B).
+            color (tuple, optional): The background color of the rectangle (B, G, R).
             txt_color (tuple, optional): The color of the text (R, G, B).
             rotated (bool, optional): Variable used to check if task is OBB
         """

ultralytics/utils/torch_utils.py

@@ -48,11 +48,12 @@ TORCHVISION_0_18 = check_version(TORCHVISION_VERSION, "0.18.0")
 def torch_distributed_zero_first(local_rank: int):
     """Ensures all processes in distributed training wait for the local master (rank 0) to complete a task first."""
     initialized = dist.is_available() and dist.is_initialized()
+
     if initialized and local_rank not in {-1, 0}:
         dist.barrier(device_ids=[local_rank])
     yield
     if initialized and local_rank == 0:
-        dist.barrier(device_ids=[0])
+        dist.barrier(device_ids=[local_rank])
 
 
 def smart_inference_mode():