Merge branch 'main' into ci_matrix_tests

.github/workflows/format.yml

@@ -1,6 +1,6 @@
-# Ultralytics 🚀, AGPL-3.0 license
-# Ultralytics Format Workflow
-# This workflow automatically formats code and documentation in pull requests and pushes to main branch
+# Ultralytics 🚀 - AGPL-3.0 license
+# Ultralytics Actions https://github.com/ultralytics/actions
+# This workflow automatically formats code and documentation in PRs to official Ultralytics standards
 
 name: Ultralytics Actions
 
@@ -14,7 +14,10 @@ jobs:
   format:
     runs-on: ubuntu-latest
     steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v4
-      - name: Run Ultralytics Formatting Actions
+      - name: Run Ultralytics Formatting
         uses: ultralytics/actions@main
+        with:
+          python: true
+          docstrings: true
+          markdown: true
+          spelling: true

README.md

@@ -91,8 +91,7 @@ Connect to the Ultralytics HUB notebook and employ your model API key to embark
 
 ## 🌐 3. Deploy to the Real World
 
-Transition your model to 13 different formats including TensorFlow, ONNX, OpenVINO, CoreML, Paddle, and more. Operate your models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or
-[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
+Transition your model to 13 different formats including TensorFlow, ONNX, OpenVINO, CoreML, Paddle, and more. Operate your models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
 
 ## ❓ Have Issues or Questions?
 

example_datasets/coco8-pose/README.md

@@ -2,14 +2,9 @@
 
 ## Introduction
 
-[Ultralytics](https://ultralytics.com) COCO8-pose is a small, but versatile pose detection dataset composed of the first
-8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and
-debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough
-to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before
-training larger datasets.
+[Ultralytics](https://ultralytics.com) COCO8-pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
-and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 ## Sample Images and Annotations
 
@@ -19,15 +14,12 @@ Here are some examples of images from the dataset, along with their correspondin
 
 ## Resources
 
-We hope that the variety of resources provided here will help you get the most out of HUB and maximize your experience
-with HUB and COCO8-pose.
+We hope that the variety of resources provided here will help you get the most out of HUB and maximize your experience with HUB and COCO8-pose.
 
 - Browse the [Docs](https://docs.ultralytics.com/) for details on usage and implementation.
 - Raise an issue on [GitHub](https://github.com/ultralytics/hub/issues/new/choose) for support and troubleshooting.
-- Join our [Discord](https://ultralytics.com/discord) community for questions and discussions with fellow users and
-  developers.
+- Join our [Discord](https://ultralytics.com/discord) community for questions and discussions with fellow users and developers.
 - Learn more about Ultralytics and our work at our [Community](https://community.ultralytics.com) page.
-- Explore the Ultralytics YOLOv8 [GitHub](https://github.com/ultralytics/ultralytics) repository for additional tools
-  and resources.
+- Explore the Ultralytics YOLOv8 [GitHub](https://github.com/ultralytics/ultralytics) repository for additional tools and resources.
 
 To request an Enterprise License, please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).

example_datasets/coco8-seg/README.md

@@ -2,14 +2,9 @@
 
 ## Introduction
 
-[Ultralytics](https://ultralytics.com) COCO8-seg is a small, but versatile instance segmentation dataset composed of the
-first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and
-debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to
-be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training
-larger datasets.
+[Ultralytics](https://ultralytics.com) COCO8-seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
-and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 ## Sample Images and Annotations
 
@@ -19,15 +14,12 @@ Here are some examples of images from the dataset, along with their correspondin
 
 ## Resources
 
-We hope that the variety of resources provided here will help you get the most out of HUB and maximize your experience
-with HUB and COCO8-seg.
+We hope that the variety of resources provided here will help you get the most out of HUB and maximize your experience with HUB and COCO8-seg.
 
 - Browse the [Docs](https://docs.ultralytics.com/) for details on usage and implementation.
 - Raise an issue on [GitHub](https://github.com/ultralytics/hub/issues/new/choose) for support and troubleshooting.
-- Join our [Discord](https://ultralytics.com/discord) community for questions and discussions with fellow users and
-  developers.
+- Join our [Discord](https://ultralytics.com/discord) community for questions and discussions with fellow users and developers.
 - Learn more about Ultralytics and our work at our [Community](https://community.ultralytics.com) page.
-- Explore the Ultralytics YOLOv8 [GitHub](https://github.com/ultralytics/ultralytics) repository for additional tools
-  and resources.
+- Explore the Ultralytics YOLOv8 [GitHub](https://github.com/ultralytics/ultralytics) repository for additional tools and resources.
 
 To request an Enterprise License, please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).

example_datasets/coco8/README.md

@@ -2,14 +2,9 @@
 
 ## Introduction
 
-[Ultralytics](https://ultralytics.com) COCO8 is a small, but versatile object detection dataset composed of the first 8
-images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging
-object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be
-easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training
-larger datasets.
+[Ultralytics](https://ultralytics.com) COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
-This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
-and [YOLOv8](https://github.com/ultralytics/ultralytics).
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 ## Sample Images and Annotations
 
@@ -19,15 +14,12 @@ Here are some examples of images from the dataset, along with their correspondin
 
 ## Resources
 
-We hope that the variety of resources provided here will help you get the most out of HUB and maximize your experience
-with HUB and COCO8.
+We hope that the variety of resources provided here will help you get the most out of HUB and maximize your experience with HUB and COCO8.
 
 - Browse the [Docs](https://docs.ultralytics.com/) for details on usage and implementation.
 - Raise an issue on [GitHub](https://github.com/ultralytics/hub/issues/new/choose) for support and troubleshooting.
-- Join our [Discord](https://ultralytics.com/discord) community for questions and discussions with fellow users and
-  developers.
+- Join our [Discord](https://ultralytics.com/discord) community for questions and discussions with fellow users and developers.
 - Learn more about Ultralytics and our work at our [Community](https://community.ultralytics.com) page.
-- Explore the Ultralytics YOLOv8 [GitHub](https://github.com/ultralytics/ultralytics) repository for additional tools
-  and resources.
+- Explore the Ultralytics YOLOv8 [GitHub](https://github.com/ultralytics/ultralytics) repository for additional tools and resources.
 
 To request an Enterprise License, please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).

requirements.txt

@@ -1,5 +1,4 @@
 # Ultralytics requirements
 # Usage: pip install -r requirements.txt
 
-# Base ----------------------------------------
-ultralytics>=8.0
+ultralytics>=8.0.232