initial yolov8to

yolov8-to/CITATION.cff

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+cff-version: 1.2.0
+preferred-citation:
+  type: software
+  message: If you use this software, please cite it as below.
+  authors:
+  - family-names: Jocher
+    given-names: Glenn
+    orcid: "https://orcid.org/0000-0001-5950-6979"
+  - family-names: Chaurasia
+    given-names: Ayush
+    orcid: "https://orcid.org/0000-0002-7603-6750"
+  - family-names: Qiu
+    given-names: Jing
+    orcid: "https://orcid.org/0000-0003-3783-7069"
+  title: "YOLO by Ultralytics"
+  version: 8.0.0
+  # doi: 10.5281/zenodo.3908559  # TODO
+  date-released: 2023-1-10
+  license: AGPL-3.0
+  url: "https://github.com/ultralytics/ultralytics"

yolov8-to/CONTRIBUTING.md

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+## Contributing to YOLOv8 🚀
+
+We love your input! We want to make contributing to YOLOv8 as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing a new feature
+- Becoming a maintainer
+
+YOLOv8 works so well due to our combined community effort, and for every small improvement you contribute you will be
+helping push the frontiers of what's possible in AI 😃!
+
+## Submitting a Pull Request (PR) 🛠️
+
+Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
+
+### 1. Select File to Update
+
+Select `requirements.txt` to update by clicking on it in GitHub.
+
+<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
+
+### 2. Click 'Edit this file'
+
+Button is in top-right corner.
+
+<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
+
+### 3. Make Changes
+
+Change `matplotlib` version from `3.2.2` to `3.3`.
+
+<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
+
+### 4. Preview Changes and Submit PR
+
+Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
+for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
+changes** button. All done, your PR is now submitted to YOLOv8 for review and approval 😃!
+
+<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
+
+### PR recommendations
+
+To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+- ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update
+  your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
+
+<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 15" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
+
+- ✅ Verify all YOLOv8 Continuous Integration (CI) **checks are passing**.
+
+<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 03" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
+
+- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
+  but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
+
+### Docstrings
+
+Not all functions or classes require docstrings but when they do, we
+follow [google-style docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings).
+Here is an example:
+
+```python
+"""
+    What the function does. Performs NMS on given detection predictions.
+
+    Args:
+        arg1: The description of the 1st argument
+        arg2: The description of the 2nd argument
+
+    Returns:
+        What the function returns. Empty if nothing is returned.
+
+    Raises:
+        Exception Class: When and why this exception can be raised by the function.
+"""
+```
+
+## Submitting a Bug Report 🐛
+
+If you spot a problem with YOLOv8 please submit a Bug Report!
+
+For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few
+short guidelines below to help users provide what we need in order to get started.
+
+When asking a question, people will be better able to provide help if you provide **code** that they can easily
+understand and use to **reproduce** the problem. This is referred to by community members as creating
+a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). Your code that reproduces
+the problem should be:
+
+- ✅ **Minimal** – Use as little code as possible that still produces the same problem
+- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
+- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
+
+In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
+should be:
+
+- ✅ **Current** – Verify that your code is up-to-date with current
+  GitHub [main](https://github.com/ultralytics/ultralytics/tree/main) branch, and if necessary `git pull` or `git clone`
+  a new copy to ensure your problem has not already been resolved by previous commits.
+- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
+  repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
+
+If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛
+**Bug Report** [template](https://github.com/ultralytics/ultralytics/issues/new/choose) and providing
+a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better
+understand and diagnose your problem.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under
+the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/)

yolov8-to/LICENSE

@@ -0,0 +1,661 @@
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yolov8-to/MANIFEST.in

@@ -0,0 +1,8 @@
+include *.md
+include requirements.txt
+include LICENSE
+include setup.py
+include ultralytics/assets/bus.jpg
+include ultralytics/assets/zidane.jpg
+recursive-include ultralytics *.yaml
+recursive-exclude __pycache__ *

yolov8-to/README.md

@@ -0,0 +1,271 @@
+<div align="center">
+  <p>
+    <a href="https://ultralytics.com/yolov8" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
+  </p>
+
+[English](README.md) | [简体中文](README.zh-CN.md)
+<br>
+
+<div>
+    <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
+    <br>
+    <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
+    <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+  </div>
+  <br>
+
+[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
+
+We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/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).
+
+<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
+
+<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://ultralytics.com/discord" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
+</div>
+</div>
+
+## <div align="center">Documentation</div>
+
+See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
+
+<details open>
+<summary>Install</summary>
+
+Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
+
+```bash
+pip install ultralytics
+```
+
+For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
+
+</details>
+
+<details open>
+<summary>Usage</summary>
+
+#### CLI
+
+YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
+
+```bash
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
+
+#### Python
+
+YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
+
+```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8n.yaml")  # build a new model from scratch
+model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+# Use the model
+model.train(data="coco128.yaml", epochs=3)  # train the model
+metrics = model.val()  # evaluate model performance on the validation set
+results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
+path = model.export(format="onnx")  # export the model to ONNX format
+```
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
+
+</details>
+
+## <div align="center">Models</div>
+
+YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
+
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
+
+All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+
+<details open><summary>Detection</summary>
+
+See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models.
+
+| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+  <br>Reproduce by `yolo val detect data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
+  <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Segmentation</summary>
+
+See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models.
+
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
+| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
+| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
+| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
+| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+  <br>Reproduce by `yolo val segment data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
+  <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Classification</summary>
+
+See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models.
+
+| Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
+| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
+| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224                   | 66.6             | 87.0             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
+| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224                   | 72.3             | 91.1             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
+| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224                   | 76.4             | 93.2             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
+| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224                   | 78.0             | 94.1             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
+| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224                   | 78.4             | 94.3             | 232.0                          | 1.01                                | 57.4               | 154.8                    |
+
+- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
+- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
+
+</details>
+
+<details><summary>Pose</summary>
+
+See [Pose Docs](https://docs.ultralytics.com/tasks/pose) for usage examples with these models.
+
+| Model                                                                                                | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt)       | 640                   | 50.4                  | 80.1               | 131.8                          | 1.18                                | 3.3                | 9.2               |
+| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt)       | 640                   | 60.0                  | 86.2               | 233.2                          | 1.42                                | 11.6               | 30.2              |
+| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |
+| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |
+| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
+| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
+  dataset.
+  <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
+  <br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
+
+</details>
+
+## <div align="center">Integrations</div>
+
+Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow.
+
+<br>
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov8-readme-comet">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
+</div>
+
+|                                                           Roboflow                                                           |                                                            ClearML ⭐ NEW                                                            |                                                                        Comet ⭐ NEW                                                                        |                                           Neural Magic ⭐ NEW                                           |
+| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
+
+## <div align="center">Ultralytics HUB</div>
+
+Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
+
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+
+## <div align="center">Contribute</div>
+
+We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a>
+
+## <div align="center">License</div>
+
+Ultralytics offers two licensing options to accommodate diverse use cases:
+
+- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/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 Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/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://ultralytics.com/discord" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
+</div>

yolov8-to/README.zh-CN.md

@@ -0,0 +1,269 @@
+<div align="center">
+  <p>
+    <a href="https://ultralytics.com/yolov8" target="_blank">
+      <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
+  </p>
+
+[English](README.md) | [简体中文](README.zh-CN.md)
+<br>
+
+<div>
+    <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
+    <a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
+    <br>
+    <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
+    <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+  </div>
+  <br>
+
+[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。
+
+我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 <a href="https://docs.ultralytics.com/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/ultralytics/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
+
+<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
+
+<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://ultralytics.com/discord" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
+</div>
+</div>
+
+## <div align="center">文档</div>
+
+请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关培训、验证、预测和部署的完整文档。
+
+<details open>
+<summary>安装</summary>
+
+使用Pip在一个[**Python>=3.8**](https://www.python.org/)环境中安装`ultralytics`包，此环境还需包含[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/)。这也会安��所有必要的[依赖项](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt)。
+
+[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
+
+```bash
+pip install ultralytics
+```
+
+如需使用包括[Conda](https://anaconda.org/conda-forge/ultralytics)、[Docker](https://hub.docker.com/r/ultralytics/ultralytics)和Git在内的其他安装方法，请参考[快速入门指南](https://docs.ultralytics.com/quickstart)。
+
+</details>
+
+<details open>
+<summary>Usage</summary>
+
+#### CLI
+
+YOLOv8 可以在命令行界面（CLI）中直接使用，只需输入 `yolo` 命令：
+
+```bash
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+`yolo` 可用于各种任务和模式，并接受其他参数，例如 `imgsz=640`。查看 YOLOv8 [CLI 文档](https://docs.ultralytics.com/usage/cli)以获取示例。
+
+#### Python
+
+YOLOv8 也可以在 Python 环境中直接使用，并接受与上述 CLI 示例中相同的[参数](https://docs.ultralytics.com/usage/cfg/)：
+
+```python
+from ultralytics import YOLO
+
+# 加载模型
+model = YOLO("yolov8n.yaml")  # 从头开始构建新模型
+model = YOLO("yolov8n.pt")  # 加载预训练模型（建议用于训练）
+
+# 使用模型
+model.train(data="coco128.yaml", epochs=3)  # 训练模型
+metrics = model.val()  # 在验证集上评估模型性能
+results = model("https://ultralytics.com/images/bus.jpg")  # 对图像进行预测
+success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
+```
+
+[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) 会自动从最新的 Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)中下载。查看 YOLOv8 [Python 文档](https://docs.ultralytics.com/usage/python)以获取更多示例。
+
+</details>
+
+## <div align="center">模型</div>
+
+在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
+
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
+
+所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时会自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。
+
+<details open><summary>检测</summary>
+
+查看 [检测文档](https://docs.ultralytics.com/tasks/detect/) 以获取使用这些模型的示例。
+
+| 模型                                                                                   | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| ------------------------------------------------------------------------------------ | --------------- | -------------------- | --------------------------- | -------------------------------- | -------------- | ----------------- |
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640             | 37.3                 | 80.4                        | 0.99                             | 3.2            | 8.7               |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640             | 44.9                 | 128.4                       | 1.20                             | 11.2           | 28.6              |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640             | 50.2                 | 234.7                       | 1.83                             | 25.9           | 78.9              |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640             | 52.9                 | 375.2                       | 2.39                             | 43.7           | 165.2             |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640             | 53.9                 | 479.1                       | 3.53                             | 68.2           | 257.8             |
+
+- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。
+  <br>通过 `yolo val detect data=coco.yaml device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
+  <br>通过 `yolo val detect data=coco128.yaml batch=1 device=0|cpu` 复现
+
+</details>
+
+<details><summary>分割</summary>
+
+查看 [分割文档](https://docs.ultralytics.com/tasks/segment/) 以获取使用这些模型的示例。
+
+| 模型                                                                                           | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | --------------- | -------------------- | --------------------- | --------------------------- | -------------------------------- | -------------- | ----------------- |
+| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640             | 36.7                 | 30.5                  | 96.1                        | 1.21                             | 3.4            | 12.6              |
+| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640             | 44.6                 | 36.8                  | 155.7                       | 1.47                             | 11.8           | 42.6              |
+| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640             | 49.9                 | 40.8                  | 317.0                       | 2.18                             | 27.3           | 110.2             |
+| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640             | 52.3                 | 42.6                  | 572.4                       | 2.79                             | 46.0           | 220.5             |
+| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640             | 53.4                 | 43.4                  | 712.1                       | 4.02                             | 71.8           | 344.1             |
+
+- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。
+  <br>通过 `yolo val segment data=coco.yaml device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
+  <br>通过 `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu` 复现
+
+</details>
+
+<details><summary>分类</summary>
+
+查看 [分类文档](https://docs.ultralytics.com/tasks/classify/) 以获取使用这些模型的示例。
+
+| 模型                                                                                           | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
+| -------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | --------------------------- | -------------------------------- | -------------- | ------------------------ |
+| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224             | 66.6             | 87.0             | 12.9                        | 0.31                             | 2.7            | 4.3                      |
+| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224             | 72.3             | 91.1             | 23.4                        | 0.35                             | 6.4            | 13.5                     |
+| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224             | 76.4             | 93.2             | 85.4                        | 0.62                             | 17.0           | 42.7                     |
+| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224             | 78.0             | 94.1             | 163.0                       | 0.87                             | 37.5           | 99.7                     |
+| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224             | 78.4             | 94.3             | 232.0                       | 1.01                             | 57.4           | 154.8                    |
+
+- **acc** 值是模型在 [ImageNet](https://www.image-net.org/) 数据集验证集上的准确率。
+  <br>通过 `yolo val classify data=path/to/ImageNet device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 ImageNet val 图像进行平均计算的。
+  <br>通过 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu` 复现
+
+</details>
+
+<details><summary>姿态</summary>
+
+查看 [姿态文档](https://docs.ultralytics.com/tasks/) 以获取使用这些模型的示例。
+
+| 模型                                                                                                   | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| ---------------------------------------------------------------------------------------------------- | --------------- | --------------------- | ------------------ | --------------------------- | -------------------------------- | -------------- | ----------------- |
+| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt)       | 640             | 50.4                  | 80.1               | 131.8                       | 1.18                             | 3.3            | 9.2               |
+| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt)       | 640             | 60.0                  | 86.2               | 233.2                       | 1.42                             | 11.6           | 30.2              |
+| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt)       | 640             | 65.0                  | 88.8               | 456.3                       | 2.00                             | 26.4           | 81.0              |
+| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt)       | 640             | 67.6                  | 90.0               | 784.5                       | 2.59                             | 44.4           | 168.6             |
+| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640             | 69.2                  | 90.2               | 1607.1                      | 3.73                             | 69.4           | 263.2             |
+| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280            | 71.6                  | 91.2               | 4088.7                      | 10.04                            | 99.1           | 1066.4            |
+
+- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](http://cocodataset.org) 数据集上的结果。
+  <br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
+- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。
+  <br>通过 `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu` 复现
+
+</details>
+
+## <div align="center">集成</div>
+
+我们与领先的AI平台的关键整合扩展了Ultralytics产品的功能，增强了数据集标签化、训练、可视化和模型管理等任务。探索Ultralytics如何与[Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic以及[OpenVINO](https://docs.ultralytics.com/integrations/openvino)合作，优化您的AI工作流程。
+
+<br>
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov8-readme-comet">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://bit.ly/yolov5-neuralmagic">
+    <img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
+</div>
+
+|                                      Roboflow                                      |                                 ClearML ⭐ NEW                                  |                                     Comet ⭐ NEW                                      |                                  Neural Magic ⭐ NEW                                   |
+| :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
+| 使用 [Roboflow](https://roboflow.com/?ref=ultralytics) 将您的自定义数据集直接标记并导出至 YOLOv8 进行训练 | 使用 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！）自���跟踪、可视化，甚至远程训练 YOLOv8 | 免费且永久，[Comet](https://bit.ly/yolov8-readme-comet) 让您保存 YOLOv8 模型、恢复训练，并以交互式方式查看和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 使 YOLOv8 推理速度提高多达 6 倍 |
+
+## <div align="center">Ultralytics HUB</div>
+
+体验 [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://ultralytics.com/app_install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！
+
+<a href="https://bit.ly/ultralytics_hub" target="_blank">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+
+## <div align="center">贡献</div>
+
+我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing)以开始使用，并填写我们的[调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
+<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a>
+
+## <div align="center">许可证</div>
+
+Ultralytics 提供两种许可证选项以适应各种使用场景：
+
+- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/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/ultralytics/blob/main/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">联系方式</div>
+
+对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/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>
+  <a href="https://ultralytics.com/discord" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
+</div>

yolov8-to/docker/Dockerfile

@@ -0,0 +1,83 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
+
+# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3
+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+RUN pip install --no-cache nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com
+
+# 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
+# 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 gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Security updates
+# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
+RUN apt upgrade --no-install-recommends -y openssl tar
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/app  (issues as not a .git directory)
+RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]" thop albumentations comet pycocotools pytest-cov
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0
+RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
+# Fix error: `np.bool` was a deprecated alias for the builtin `bool`
+RUN pip install --no-cache numpy==1.23.5
+# Remove exported models
+RUN rm -rf tmp
+
+# Set environment variables
+ENV OMP_NUM_THREADS=1
+# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library" https://github.com/pytorch/pytorch/issues/37377
+ENV MKL_THREADING_LAYER=GNU
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
+
+# Pull and Run with access to all GPUs
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
+
+# Pull and Run with access to GPUs 2 and 3 (inside container CUDA devices will appear as 0 and 1)
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus '"device=2,3"' $t
+
+# Pull and Run with local directory access
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
+
+# Kill all
+# sudo docker kill $(sudo docker ps -q)
+
+# Kill all image-based
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/ultralytics:latest)
+
+# DockerHub tag update
+# t=ultralytics/ultralytics:latest tnew=ultralytics/ultralytics:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
+
+# Clean up
+# sudo docker system prune -a --volumes
+
+# Update Ubuntu drivers
+# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
+
+# DDP test
+# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
+
+# GCP VM from Image
+# docker.io/ultralytics/ultralytics:latest

yolov8-to/docker/Dockerfile-arm64

@@ -0,0 +1,39 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM arm64v8/ubuntu:22.10
+
+# 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
+# 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 gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/app  (issues as not a .git directory)
+RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e . thop
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-arm64 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

yolov8-to/docker/Dockerfile-cpu

@@ -0,0 +1,49 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM ubuntu:lunar-20230615
+
+# 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
+# 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-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/app  (issues as not a .git directory)
+RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
+
+# 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
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]" thop --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0
+# RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
+# Remove exported models
+RUN rm -rf tmp
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-cpu && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-cpu && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

yolov8-to/docker/Dockerfile-jetson

@@ -0,0 +1,46 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack for YOLOv8 on Jetson Nano, TX1/TX2, Xavier NX, AGX Xavier, AGX Orin, and Orin NX
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch
+FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
+
+# 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
+# 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 gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/app  (issues as not a .git directory)
+RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
+
+# Remove opencv-python from requirements.txt as it conflicts with opencv-python installed in base image
+RUN grep -v '^opencv-python' requirements.txt > tmp.txt && mv tmp.txt requirements.txt
+
+# Install pip packages manually for TensorRT compatibility https://github.com/NVIDIA/TensorRT/issues/2567
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache tqdm matplotlib pyyaml psutil pandas onnx thop "numpy==1.23"
+RUN pip install --no-cache -e .
+
+# Set environment variables
+ENV OMP_NUM_THREADS=1
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

yolov8-to/docker/Dockerfile-python

@@ -0,0 +1,49 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
+
+# Use the official Python 3.10 slim-bookworm as base image
+FROM python:3.10-slim-bookworm
+
+# 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
+# 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-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/app  (issues as not a .git directory)
+RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
+
+# 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
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]" thop --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0
+RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
+# Remove exported models
+RUN rm -rf tmp
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-python && sudo docker build -f docker/Dockerfile-python -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-python && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

yolov8-to/docker/Dockerfile-runner

@@ -0,0 +1,37 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference tests
+
+# Start FROM Ultralytics GPU image
+FROM ultralytics/ultralytics:latest
+
+# Set the working directory
+WORKDIR /actions-runner
+
+# Download and unpack the latest runner
+RUN curl -o actions-runner-linux-x64-2.308.0.tar.gz -L https://github.com/actions/runner/releases/download/v2.308.0/actions-runner-linux-x64-2.308.0.tar.gz && \
+    tar xzf actions-runner-linux-x64-2.308.0.tar.gz && \
+    rm actions-runner-linux-x64-2.308.0.tar.gz
+
+# Install runner dependencies
+ENV RUNNER_ALLOW_RUNASROOT=1
+ENV DEBIAN_FRONTEND=noninteractive
+RUN ./bin/installdependencies.sh && \
+    apt-get -y install libicu-dev
+
+# Inline ENTRYPOINT command to configure and start runner with default TOKEN and NAME
+ENTRYPOINT sh -c './config.sh --url https://github.com/ultralytics/ultralytics \
+                              --token ${GITHUB_RUNNER_TOKEN:-TOKEN} \
+                              --name ${GITHUB_RUNNER_NAME:-NAME} \
+                              --labels gpu-latest \
+                              --replace && \
+                  ./run.sh'
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-runner && sudo docker build -f docker/Dockerfile-runner -t $t . && sudo docker push $t
+
+# Pull and Run in detached mode with access to GPUs 0 and 1
+# t=ultralytics/ultralytics:latest-runner && sudo docker run -d -e GITHUB_RUNNER_TOKEN=TOKEN -e GITHUB_RUNNER_NAME=NAME --ipc=host --gpus '"device=0,1"' $t

yolov8-to/docs/CNAME

@@ -0,0 +1 @@
+docs.ultralytics.com

yolov8-to/docs/README.md

@@ -0,0 +1,90 @@
+---
+description: Learn how to install Ultralytics in developer mode, build and serve it locally for testing, and deploy your documentation site on platforms like GitHub Pages, GitLab Pages, and Amazon S3.
+keywords: Ultralytics, documentation, mkdocs, installation, developer mode, building, deployment, local server, GitHub Pages, GitLab Pages, Amazon S3
+---
+
+# Ultralytics Docs
+
+Ultralytics Docs are deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com).
+
+### Install Ultralytics package
+
+To install the ultralytics package in developer mode, you will need to have Git and Python 3 installed on your system.
+Then, follow these steps:
+
+1. Clone the ultralytics repository to your local machine using Git:
+
+```bash
+git clone https://github.com/ultralytics/ultralytics.git
+```
+
+2. Navigate to the root directory of the repository:
+
+```bash
+cd ultralytics
+```
+
+3. Install the package in developer mode using pip:
+
+```bash
+pip install -e ".[dev]"
+```
+
+This will install the ultralytics package and its dependencies in developer mode, allowing you to make changes to the
+package code and have them reflected immediately in your Python environment.
+
+Note that you may need to use the pip3 command instead of pip if you have multiple versions of Python installed on your
+system.
+
+### Building and Serving Locally
+
+The `mkdocs serve` command is used to build and serve a local version of the MkDocs documentation site. It is typically
+used during the development and testing phase of a documentation project.
+
+```bash
+mkdocs serve
+```
+
+Here is a breakdown of what this command does:
+
+- `mkdocs`: This is the command-line interface (CLI) for the MkDocs static site generator. It is used to build and serve
+  MkDocs sites.
+- `serve`: This is a subcommand of the `mkdocs` CLI that tells it to build and serve the documentation site locally.
+- `-a`: This flag specifies the hostname and port number to bind the server to. The default value is `localhost:8000`.
+- `-t`: This flag specifies the theme to use for the documentation site. The default value is `mkdocs`.
+- `-s`: This flag tells the `serve` command to serve the site in silent mode, which means it will not display any log
+  messages or progress updates.
+  When you run the `mkdocs serve` command, it will build the documentation site using the files in the `docs/` directory
+  and serve it at the specified hostname and port number. You can then view the site by going to the URL in your web
+  browser.
+
+While the site is being served, you can make changes to the documentation files and see them reflected in the live site
+immediately. This is useful for testing and debugging your documentation before deploying it to a live server.
+
+To stop the serve command and terminate the local server, you can use the `CTRL+C` keyboard shortcut.
+
+### Deploying Your Documentation Site
+
+To deploy your MkDocs documentation site, you will need to choose a hosting provider and a deployment method. Some
+popular options include GitHub Pages, GitLab Pages, and Amazon S3.
+
+Before you can deploy your site, you will need to configure your `mkdocs.yml` file to specify the remote host and any
+other necessary deployment settings.
+
+Once you have configured your `mkdocs.yml` file, you can use the `mkdocs deploy` command to build and deploy your site.
+This command will build the documentation site using the files in the `docs/` directory and the specified configuration
+file and theme, and then deploy the site to the specified remote host.
+
+For example, to deploy your site to GitHub Pages using the gh-deploy plugin, you can use the following command:
+
+```bash
+mkdocs gh-deploy
+```
+
+If you are using GitHub Pages, you can set a custom domain for your documentation site by going to the "Settings" page
+for your repository and updating the "Custom domain" field in the "GitHub Pages" section.
+
+![196814117-fc16e711-d2be-4722-9536-b7c6d78fd167](https://user-images.githubusercontent.com/26833433/210150206-9e86dcd7-10af-43e4-9eb2-9518b3799eac.png)
+
+For more information on deploying your MkDocs documentation site, see
+the [MkDocs documentation](https://www.mkdocs.org/user-guide/deploying-your-docs/).

yolov8-to/docs/SECURITY.md

@@ -0,0 +1,26 @@
+---
+description: Discover how Ultralytics ensures the safety of user data and systems. Check out the measures we have implemented, including Snyk and GitHub CodeQL Scanning.
+keywords: Ultralytics, Security Policy, data security, open-source projects, Snyk scanning, CodeQL scanning, vulnerability detection, threat prevention
+---
+
+# Security Policy
+
+At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities.
+
+## Snyk Scanning
+
+We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan all Ultralytics repositories for vulnerabilities and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any risks to our users.
+
+[![ultralytics](https://snyk.io/advisor/python/ultralytics/badge.svg)](https://snyk.io/advisor/python/ultralytics)
+
+## GitHub CodeQL Scanning
+
+In addition to our Snyk scans, we also use GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) scans to proactively identify and address security vulnerabilities across all Ultralytics repositories.
+
+[![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml)
+
+## Reporting Security Issues
+
+If you suspect or discover a security vulnerability in any of our repositories, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible.
+
+We appreciate your help in keeping all Ultralytics open-source projects secure and safe for everyone.

yolov8-to/docs/build_reference.py

@@ -0,0 +1,126 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""
+Helper file to build Ultralytics Docs reference section. Recursively walks through ultralytics dir and builds an MkDocs
+reference section of *.md files composed of classes and functions, and also creates a nav menu for use in mkdocs.yaml.
+
+Note: Must be run from repository root directory. Do not run from docs directory.
+"""
+
+import os
+import re
+from collections import defaultdict
+from pathlib import Path
+
+from ultralytics.utils import ROOT
+
+NEW_YAML_DIR = ROOT.parent
+CODE_DIR = ROOT
+REFERENCE_DIR = ROOT.parent / 'docs/reference'
+
+
+def extract_classes_and_functions(filepath):
+    with open(filepath, 'r') as file:
+        content = file.read()
+
+    class_pattern = r'(?:^|\n)class\s(\w+)(?:\(|:)'
+    func_pattern = r'(?:^|\n)def\s(\w+)\('
+
+    classes = re.findall(class_pattern, content)
+    functions = re.findall(func_pattern, content)
+
+    return classes, functions
+
+
+def create_markdown(py_filepath, module_path, classes, functions):
+    md_filepath = py_filepath.with_suffix('.md')
+
+    # Read existing content and keep header content between first two ---
+    header_content = ''
+    if md_filepath.exists():
+        with open(md_filepath, 'r') as file:
+            existing_content = file.read()
+            header_parts = existing_content.split('---')
+            for part in header_parts:
+                if 'description:' in part or 'comments:' in part:
+                    header_content += f'---{part}---\n\n'
+
+    module_name = module_path.replace('.__init__', '')
+    module_path = module_path.replace(".", "/")
+    url = f'https://github.com/ultralytics/ultralytics/blob/main/{module_path}.py'
+    title_content = (f'# Reference for `{module_path}.py`\n\n'
+                     f'!!! note\n\n'
+                     f'    Full source code for this file is available at [{url}]({url}). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!\n\n')
+    md_content = [f'---\n## ::: {module_name}.{class_name}\n<br><br>\n' for class_name in classes]
+    md_content.extend(f'---\n## ::: {module_name}.{func_name}\n<br><br>\n' for func_name in functions)
+    md_content = header_content + title_content + '\n'.join(md_content)
+    if not md_content.endswith('\n'):
+        md_content += '\n'
+
+    os.makedirs(os.path.dirname(md_filepath), exist_ok=True)
+    with open(md_filepath, 'w') as file:
+        file.write(md_content)
+
+    return md_filepath.relative_to(NEW_YAML_DIR)
+
+
+def nested_dict():
+    return defaultdict(nested_dict)
+
+
+def sort_nested_dict(d):
+    return {
+        key: sort_nested_dict(value) if isinstance(value, dict) else value
+        for key, value in sorted(d.items())
+    }
+
+
+def create_nav_menu_yaml(nav_items):
+    nav_tree = nested_dict()
+
+    for item_str in nav_items:
+        item = Path(item_str)
+        parts = item.parts
+        current_level = nav_tree['reference']
+        for part in parts[2:-1]:  # skip the first two parts (docs and reference) and the last part (filename)
+            current_level = current_level[part]
+
+        md_file_name = parts[-1].replace('.md', '')
+        current_level[md_file_name] = item
+
+    nav_tree_sorted = sort_nested_dict(nav_tree)
+
+    def _dict_to_yaml(d, level=0):
+        yaml_str = ''
+        indent = '  ' * level
+        for k, v in d.items():
+            if isinstance(v, dict):
+                yaml_str += f'{indent}- {k}:\n{_dict_to_yaml(v, level + 1)}'
+            else:
+                yaml_str += f"{indent}- {k}: {str(v).replace('docs/', '')}\n"
+        return yaml_str
+
+    with open(NEW_YAML_DIR / 'nav_menu_updated.yml', 'w') as file:
+        yaml_str = _dict_to_yaml(nav_tree_sorted)
+        file.write(yaml_str)
+
+
+def main():
+    nav_items = []
+    for root, _, files in os.walk(CODE_DIR):
+        for file in files:
+            if file.endswith('.py'):
+                py_filepath = Path(root) / file
+                classes, functions = extract_classes_and_functions(py_filepath)
+
+                if classes or functions:
+                    py_filepath_rel = py_filepath.relative_to(CODE_DIR)
+                    md_filepath = REFERENCE_DIR / py_filepath_rel
+                    module_path = f"ultralytics.{py_filepath_rel.with_suffix('').as_posix().replace('/', '.')}"
+                    md_rel_filepath = create_markdown(md_filepath, module_path, classes, functions)
+                    nav_items.append(str(md_rel_filepath))
+
+    create_nav_menu_yaml(nav_items)
+
+
+if __name__ == '__main__':
+    main()

yolov8-to/docs/datasets/classify/caltech101.md

@@ -0,0 +1,81 @@
+---
+comments: true
+description: Learn about the Caltech-101 dataset, its structure and uses in machine learning. Includes instructions to train a YOLO model using this dataset.
+keywords: Caltech-101, dataset, YOLO training, machine learning, object recognition, ultralytics
+---
+
+# Caltech-101 Dataset
+
+The [Caltech-101](https://data.caltech.edu/records/mzrjq-6wc02) dataset is a widely used dataset for object recognition tasks, containing around 9,000 images from 101 object categories. The categories were chosen to reflect a variety of real-world objects, and the images themselves were carefully selected and annotated to provide a challenging benchmark for object recognition algorithms.
+
+## Key Features
+
+- The Caltech-101 dataset comprises around 9,000 color images divided into 101 categories.
+- The categories encompass a wide variety of objects, including animals, vehicles, household items, and people.
+- The number of images per category varies, with about 40 to 800 images in each category.
+- Images are of variable sizes, with most images being medium resolution.
+- Caltech-101 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.
+
+## Dataset Structure
+
+Unlike many other datasets, the Caltech-101 dataset is not formally split into training and testing sets. Users typically create their own splits based on their specific needs. However, a common practice is to use a random subset of images for training (e.g., 30 images per category) and the remaining images for testing.
+
+## Applications
+
+The Caltech-101 dataset is extensively used for training and evaluating deep learning models in object recognition tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its wide variety of categories and high-quality images make it an excellent dataset for research and development in the field of machine learning and computer vision.
+
+## Usage
+
+To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='caltech101', epochs=100, imgsz=416)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
+        ```
+
+## Sample Images and Annotations
+
+The Caltech-101 dataset contains high-quality color images of various objects, providing a well-structured dataset for object recognition tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239366386-44171121-b745-4206-9b59-a3be41e16089.png)
+
+The example showcases the variety and complexity of the objects in the Caltech-101 dataset, emphasizing the significance of a diverse dataset for training robust object recognition models.
+
+## Citations and Acknowledgments
+
+If you use the Caltech-101 dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{fei2007learning,
+          title={Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories},
+          author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
+          journal={Computer vision and Image understanding},
+          volume={106},
+          number={1},
+          pages={59--70},
+          year={2007},
+          publisher={Elsevier}
+        }
+        ```
+
+We would like to acknowledge Li Fei-Fei, Rob Fergus, and Pietro Perona for creating and maintaining the Caltech-101 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the Caltech-101 dataset and its creators, visit the [Caltech-101 dataset website](https://data.caltech.edu/records/mzrjq-6wc02).

yolov8-to/docs/datasets/classify/caltech256.md

@@ -0,0 +1,78 @@
+---
+comments: true
+description: Explore the Caltech-256 dataset, a diverse collection of images used for object recognition tasks in machine learning. Learn to train a YOLO model on the dataset.
+keywords: Ultralytics, YOLO, Caltech-256, dataset, object recognition, machine learning, computer vision, deep learning
+---
+
+# Caltech-256 Dataset
+
+The [Caltech-256](https://data.caltech.edu/records/nyy15-4j048) dataset is an extensive collection of images used for object classification tasks. It contains around 30,000 images divided into 257 categories (256 object categories and 1 background category). The images are carefully curated and annotated to provide a challenging and diverse benchmark for object recognition algorithms.
+
+## Key Features
+
+- The Caltech-256 dataset comprises around 30,000 color images divided into 257 categories.
+- Each category contains a minimum of 80 images.
+- The categories encompass a wide variety of real-world objects, including animals, vehicles, household items, and people.
+- Images are of variable sizes and resolutions.
+- Caltech-256 is widely used for training and testing in the field of machine learning, particularly for object recognition tasks.
+
+## Dataset Structure
+
+Like Caltech-101, the Caltech-256 dataset does not have a formal split between training and testing sets. Users typically create their own splits according to their specific needs. A common practice is to use a random subset of images for training and the remaining images for testing.
+
+## Applications
+
+The Caltech-256 dataset is extensively used for training and evaluating deep learning models in object recognition tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. Its diverse set of categories and high-quality images make it an invaluable dataset for research and development in the field of machine learning and computer vision.
+
+## Usage
+
+To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='caltech256', epochs=100, imgsz=416)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=caltech256 model=yolov8n-cls.pt epochs=100 imgsz=416
+        ```
+
+## Sample Images and Annotations
+
+The Caltech-256 dataset contains high-quality color images of various objects, providing a comprehensive dataset for object recognition tasks. Here are some examples of images from the dataset ([credit](https://ml4a.github.io/demos/tsne_viewer.html)):
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239365061-1e5f7857-b1e8-44ca-b3d7-d0befbcd33f9.jpg)
+
+The example showcases the diversity and complexity of the objects in the Caltech-256 dataset, emphasizing the importance of a varied dataset for training robust object recognition models.
+
+## Citations and Acknowledgments
+
+If you use the Caltech-256 dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{griffin2007caltech,
+                 title={Caltech-256 object category dataset},
+                 author={Griffin, Gregory and Holub, Alex and Perona, Pietro},
+                 year={2007}
+        }
+        ```
+
+We would like to acknowledge Gregory Griffin, Alex Holub, and Pietro Perona for creating and maintaining the Caltech-256 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the
+
+Caltech-256 dataset and its creators, visit the [Caltech-256 dataset website](https://data.caltech.edu/records/nyy15-4j048).

yolov8-to/docs/datasets/classify/cifar10.md

@@ -0,0 +1,80 @@
+---
+comments: true
+description: Explore the CIFAR-10 dataset, widely used for training in machine learning and computer vision, and learn how to use it with Ultralytics YOLO.
+keywords: CIFAR-10, dataset, machine learning, image classification, computer vision, YOLO, Ultralytics, training, testing, deep learning, Convolutional Neural Networks, Support Vector Machines
+---
+
+# CIFAR-10 Dataset
+
+The [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a collection of images used widely for machine learning and computer vision algorithms. It was developed by researchers at the CIFAR institute and consists of 60,000 32x32 color images in 10 different classes.
+
+## Key Features
+
+- The CIFAR-10 dataset consists of 60,000 images, divided into 10 classes.
+- Each class contains 6,000 images, split into 5,000 for training and 1,000 for testing.
+- The images are colored and of size 32x32 pixels.
+- The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
+- CIFAR-10 is commonly used for training and testing in the field of machine learning and computer vision.
+
+## Dataset Structure
+
+The CIFAR-10 dataset is split into two subsets:
+
+1. **Training Set**: This subset contains 50,000 images used for training machine learning models.
+2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
+
+## Applications
+
+The CIFAR-10 dataset is widely used for training and evaluating deep learning models in image classification tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a well-rounded dataset for research and development in the field of machine learning and computer vision.
+
+## Usage
+
+To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='cifar10', epochs=100, imgsz=32)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=cifar10 model=yolov8n-cls.pt epochs=100 imgsz=32
+        ```
+
+## Sample Images and Annotations
+
+The CIFAR-10 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://miro.medium.com/max/1100/1*SZnidBt7CQ4Xqcag6rd8Ew.png)
+
+The example showcases the variety and complexity of the objects in the CIFAR-10 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## Citations and Acknowledgments
+
+If you use the CIFAR-10 dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @TECHREPORT{Krizhevsky09learningmultiple,
+                    author={Alex Krizhevsky},
+                    title={Learning multiple layers of features from tiny images},
+                    institution={},
+                    year={2009}
+        }
+        ```
+
+We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-10 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-10 dataset and its creator, visit the [CIFAR-10 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).

yolov8-to/docs/datasets/classify/cifar100.md

@@ -0,0 +1,80 @@
+---
+comments: true
+description: Discover how to leverage the CIFAR-100 dataset for machine learning and computer vision tasks with YOLO. Gain insights on its structure, use, and utilization for model training.
+keywords: Ultralytics, YOLO, CIFAR-100 dataset, image classification, machine learning, computer vision, YOLO model training
+---
+
+# CIFAR-100 Dataset
+
+The [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) (Canadian Institute For Advanced Research) dataset is a significant extension of the CIFAR-10 dataset, composed of 60,000 32x32 color images in 100 different classes. It was developed by researchers at the CIFAR institute, offering a more challenging dataset for more complex machine learning and computer vision tasks.
+
+## Key Features
+
+- The CIFAR-100 dataset consists of 60,000 images, divided into 100 classes.
+- Each class contains 600 images, split into 500 for training and 100 for testing.
+- The images are colored and of size 32x32 pixels.
+- The 100 different classes are grouped into 20 coarse categories for higher level classification.
+- CIFAR-100 is commonly used for training and testing in the field of machine learning and computer vision.
+
+## Dataset Structure
+
+The CIFAR-100 dataset is split into two subsets:
+
+1. **Training Set**: This subset contains 50,000 images used for training machine learning models.
+2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
+
+## Applications
+
+The CIFAR-100 dataset is extensively used for training and evaluating deep learning models in image classification tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. The diversity of the dataset in terms of classes and the presence of color images make it a more challenging and comprehensive dataset for research and development in the field of machine learning and computer vision.
+
+## Usage
+
+To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='cifar100', epochs=100, imgsz=32)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32
+        ```
+
+## Sample Images and Annotations
+
+The CIFAR-100 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239363319-62ebf02f-7469-4178-b066-ccac3cd334db.jpg)
+
+The example showcases the variety and complexity of the objects in the CIFAR-100 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## Citations and Acknowledgments
+
+If you use the CIFAR-100 dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @TECHREPORT{Krizhevsky09learningmultiple,
+                    author={Alex Krizhevsky},
+                    title={Learning multiple layers of features from tiny images},
+                    institution={},
+                    year={2009}
+        }
+        ```
+
+We would like to acknowledge Alex Krizhevsky for creating and maintaining the CIFAR-100 dataset as a valuable resource for the machine learning and computer vision research community. For more information about the CIFAR-100 dataset and its creator, visit the [CIFAR-100 dataset website](https://www.cs.toronto.edu/~kriz/cifar.html).

yolov8-to/docs/datasets/classify/fashion-mnist.md

@@ -0,0 +1,79 @@
+---
+comments: true
+description: Learn how to use the Fashion-MNIST dataset for image classification with the Ultralytics YOLO model. Covers dataset structure, labels, applications, and usage.
+keywords: Ultralytics, YOLO, Fashion-MNIST, dataset, image classification, machine learning, deep learning, neural networks, training, testing
+---
+
+# Fashion-MNIST Dataset
+
+The [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset is a database of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms.
+
+## Key Features
+
+- Fashion-MNIST contains 60,000 training images and 10,000 testing images of Zalando's article images.
+- The dataset comprises grayscale images of size 28x28 pixels.
+- Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.
+- Fashion-MNIST is widely used for training and testing in the field of machine learning, especially for image classification tasks.
+
+## Dataset Structure
+
+The Fashion-MNIST dataset is split into two subsets:
+
+1. **Training Set**: This subset contains 60,000 images used for training machine learning models.
+2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
+
+## Labels
+
+Each training and test example is assigned to one of the following labels:
+
+0. T-shirt/top
+1. Trouser
+2. Pullover
+3. Dress
+4. Coat
+5. Sandal
+6. Shirt
+7. Sneaker
+8. Bag
+9. Ankle boot
+
+## Applications
+
+The Fashion-MNIST dataset is widely used for training and evaluating deep learning models in image classification tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. The dataset's simple and well-structured format makes it an essential resource for researchers and practitioners in the field of machine learning and computer vision.
+
+## Usage
+
+To train a CNN model on the Fashion-MNIST dataset for 100 epochs with an image size of 28x28, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='fashion-mnist', epochs=100, imgsz=28)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        ```
+
+## Sample Images and Annotations
+
+The Fashion-MNIST dataset contains grayscale images of Zalando's article images, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239359139-ce0a434e-9056-43e0-a306-3214f193dcce.png)
+
+The example showcases the variety and complexity of the images in the Fashion-MNIST dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## Acknowledgments
+
+If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.

yolov8-to/docs/datasets/classify/imagenet.md

@@ -0,0 +1,83 @@
+---
+comments: true
+description: Understand how to use ImageNet, an extensive annotated image dataset for object recognition research, with Ultralytics YOLO models. Learn about its structure, usage, and significance in computer vision.
+keywords: Ultralytics, YOLO, ImageNet, dataset, object recognition, deep learning, computer vision, machine learning, dataset training, model training, image classification, object detection
+---
+
+# ImageNet Dataset
+
+[ImageNet](https://www.image-net.org/) is a large-scale database of annotated images designed for use in visual object recognition research. It contains over 14 million images, with each image annotated using WordNet synsets, making it one of the most extensive resources available for training deep learning models in computer vision tasks.
+
+## Key Features
+
+- ImageNet contains over 14 million high-resolution images spanning thousands of object categories.
+- The dataset is organized according to the WordNet hierarchy, with each synset representing a category.
+- ImageNet is widely used for training and benchmarking in the field of computer vision, particularly for image classification and object detection tasks.
+- The annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been instrumental in advancing computer vision research.
+
+## Dataset Structure
+
+The ImageNet dataset is organized using the WordNet hierarchy. Each node in the hierarchy represents a category, and each category is described by a synset (a collection of synonymous terms). The images in ImageNet are annotated with one or more synsets, providing a rich resource for training models to recognize various objects and their relationships.
+
+## ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
+
+The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](http://image-net.org/challenges/LSVRC/) has been an important event in the field of computer vision. It has provided a platform for researchers and developers to evaluate their algorithms and models on a large-scale dataset with standardized evaluation metrics. The ILSVRC has led to significant advancements in the development of deep learning models for image classification, object detection, and other computer vision tasks.
+
+## Applications
+
+The ImageNet dataset is widely used for training and evaluating deep learning models in various computer vision tasks, such as image classification, object detection, and object localization. Some popular deep learning architectures, such as AlexNet, VGG, and ResNet, were developed and benchmarked using the ImageNet dataset.
+
+## Usage
+
+To train a deep learning model on the ImageNet dataset for 100 epochs with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='imagenet', epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        ```
+
+## Sample Images and Annotations
+
+The ImageNet dataset contains high-resolution images spanning thousands of object categories, providing a diverse and extensive dataset for training and evaluating computer vision models. Here are some examples of images from the dataset:
+
+![Dataset sample images](https://user-images.githubusercontent.com/26833433/239280348-3d8f30c7-6f05-4dda-9cfe-d62ad9faecc9.png)
+
+The example showcases the variety and complexity of the images in the ImageNet dataset, highlighting the importance of a diverse dataset for training robust computer vision models.
+
+## Citations and Acknowledgments
+
+If you use the ImageNet dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{ILSVRC15,
+                 author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
+                 title={ImageNet Large Scale Visual Recognition Challenge},
+                 year={2015},
+                 journal={International Journal of Computer Vision (IJCV)},
+                 volume={115},
+                 number={3},
+                 pages={211-252}
+        }
+        ```
+
+We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).

yolov8-to/docs/datasets/classify/imagenet10.md

@@ -0,0 +1,78 @@
+---
+comments: true
+description: Explore the compact ImageNet10 Dataset developed by Ultralytics. Ideal for fast testing of computer vision training pipelines and CV model sanity checks.
+keywords: Ultralytics, YOLO, ImageNet10 Dataset, Image detection, Deep Learning, ImageNet, AI model testing, Computer vision, Machine learning
+---
+
+# ImageNet10 Dataset
+
+The [ImageNet10](https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://ultralytics.com) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset.
+
+## Key Features
+
+- ImageNet10 is a compact version of ImageNet, with 20 images representing the first 10 classes of the original dataset.
+- The dataset is organized according to the WordNet hierarchy, mirroring the structure of the full ImageNet dataset.
+- It is ideally suited for CI tests, sanity checks, and rapid testing of training pipelines in computer vision tasks.
+- Although not designed for model benchmarking, it can provide a quick indication of a model's basic functionality and correctness.
+
+## Dataset Structure
+
+The ImageNet10 dataset, like the original ImageNet, is organized using the WordNet hierarchy. Each of the 10 classes in ImageNet10 is described by a synset (a collection of synonymous terms). The images in ImageNet10 are annotated with one or more synsets, providing a compact resource for testing models to recognize various objects and their relationships.
+
+## Applications
+
+The ImageNet10 dataset is useful for quickly testing and debugging computer vision models and pipelines. Its small size allows for rapid iteration, making it ideal for continuous integration tests and sanity checks. It can also be used for fast preliminary testing of new models or changes to existing models before moving on to full-scale testing with the complete ImageNet dataset.
+
+## Usage
+
+To test a deep learning model on the ImageNet10 dataset with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Test Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='imagenet10', epochs=5, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo train data=imagenet10 model=yolov8n-cls.pt epochs=5 imgsz=224
+        ```
+
+## Sample Images and Annotations
+
+The ImageNet10 dataset contains a subset of images from the original ImageNet dataset. These images are chosen to represent the first 10 classes in the dataset, providing a diverse yet compact dataset for quick testing and evaluation.
+
+![Dataset sample images](https://user-images.githubusercontent.com/26833433/239689723-16f9b4a7-becc-4deb-b875-d3e5c28eb03b.png)
+The example showcases the variety and complexity of the images in the ImageNet10 dataset, highlighting its usefulness for sanity checks and quick testing of computer vision models.
+
+## Citations and Acknowledgments
+
+If you use the ImageNet10 dataset in your research or development work, please cite the original ImageNet paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{ILSVRC15,
+                 author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
+                 title={ImageNet Large Scale Visual Recognition Challenge},
+                 year={2015},
+                 journal={International Journal of Computer Vision (IJCV)},
+                 volume={115},
+                 number={3},
+                 pages={211-252}
+        }
+        ```
+
+We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset. The ImageNet10 dataset, while a compact subset, is a valuable resource for quick testing and debugging in the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).

yolov8-to/docs/datasets/classify/imagenette.md

@@ -0,0 +1,113 @@
+---
+comments: true
+description: Learn about the ImageNette dataset and its usage in deep learning model training. Find code snippets for model training and explore ImageNette datatypes.
+keywords: ImageNette dataset, Ultralytics, YOLO, Image classification, Machine Learning, Deep learning, Training code snippets, CNN, ImageNette160, ImageNette320
+---
+
+# ImageNette Dataset
+
+The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [Imagenet](http://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of Imagenet for software development and education.
+
+## Key Features
+
+- ImageNette contains images from 10 different classes such as tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute.
+- The dataset comprises colored images of varying dimensions.
+- ImageNette is widely used for training and testing in the field of machine learning, especially for image classification tasks.
+
+## Dataset Structure
+
+The ImageNette dataset is split into two subsets:
+
+1. **Training Set**: This subset contains several thousands of images used for training machine learning models. The exact number varies per class.
+2. **Validation Set**: This subset consists of several hundreds of images used for validating and benchmarking the trained models. Again, the exact number varies per class.
+
+## Applications
+
+The ImageNette dataset is widely used for training and evaluating deep learning models in image classification tasks, such as Convolutional Neural Networks (CNNs), and various other machine learning algorithms. The dataset's straightforward format and well-chosen classes make it a handy resource for both beginner and experienced practitioners in the field of machine learning and computer vision.
+
+## Usage
+
+To train a model on the ImageNette dataset for 100 epochs with a standard image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='imagenette', epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=imagenette model=yolov8n-cls.pt epochs=100 imgsz=224
+        ```
+
+## Sample Images and Annotations
+
+The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://docs.fast.ai/22_tutorial.imagenette_files/figure-html/cell-21-output-1.png)
+
+The example showcases the variety and complexity of the images in the ImageNette dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## ImageNette160 and ImageNette320
+
+For faster prototyping and training, the ImageNette dataset is also available in two reduced sizes: ImageNette160 and ImageNette320. These datasets maintain the same classes and structure as the full ImageNette dataset, but the images are resized to a smaller dimension. As such, these versions of the dataset are particularly useful for preliminary model testing, or when computational resources are limited.
+
+To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imagenette320' in the training command. The following code snippets illustrate this:
+
+!!! example "Train Example with ImageNette160"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model with ImageNette160
+        results = model.train(data='imagenette160', epochs=100, imgsz=160)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model with ImageNette160
+        yolo detect train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160
+        ```
+
+!!! example "Train Example with ImageNette320"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model with ImageNette320
+        results = model.train(data='imagenette320', epochs=100, imgsz=320)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model with ImageNette320
+        yolo detect 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.
+
+## Citations and Acknowledgments
+
+If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette).

yolov8-to/docs/datasets/classify/imagewoof.md

@@ -0,0 +1,84 @@
+---
+comments: true
+description: Explore the ImageWoof dataset, designed for challenging dog breed classification. Train AI models with Ultralytics YOLO using this dataset.
+keywords: ImageWoof, image classification, dog breeds, machine learning, deep learning, Ultralytics, YOLO, dataset
+---
+
+# ImageWoof Dataset
+
+The [ImageWoof](https://github.com/fastai/imagenette) dataset is a subset of the ImageNet consisting of 10 classes that are challenging to classify, since they're all dog breeds. It was created as a more difficult task for image classification algorithms to solve, aiming at encouraging development of more advanced models.
+
+## Key Features
+
+- ImageWoof contains images of 10 different dog breeds: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, and Old English sheepdog.
+- The dataset provides images at various resolutions (full size, 320px, 160px), accommodating for different computational capabilities and research needs.
+- It also includes a version with noisy labels, providing a more realistic scenario where labels might not always be reliable.
+
+## Dataset Structure
+
+The ImageWoof dataset structure is based on the dog breed classes, with each breed having its own directory of images.
+
+## Applications
+
+The ImageWoof dataset is widely used for training and evaluating deep learning models in image classification tasks, especially when it comes to more complex and similar classes. The dataset's challenge lies in the subtle differences between the dog breeds, pushing the limits of model's performance and generalization.
+
+## Usage
+
+To train a CNN model on the ImageWoof dataset for 100 epochs with an image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='imagewoof', epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
+        ```
+
+## Dataset Variants
+
+ImageWoof dataset comes in three different sizes to accommodate various research needs and computational capabilities:
+
+1. **Full Size (imagewoof)**: This is the original version of the ImageWoof dataset. It contains full-sized images and is ideal for final training and performance benchmarking.
+
+2. **Medium Size (imagewoof320)**: This version contains images resized to have a maximum edge length of 320 pixels. It's suitable for faster training without significantly sacrificing model performance.
+
+3. **Small Size (imagewoof160)**: This version contains images resized to have a maximum edge length of 160 pixels. It's designed for rapid prototyping and experimentation where training speed is a priority.
+
+To use these variants in your training, simply replace 'imagewoof' in the dataset argument with 'imagewoof320' or 'imagewoof160'. For example:
+
+```python
+# For medium-sized dataset
+model.train(data='imagewoof320', epochs=100, imgsz=224)
+
+# For small-sized dataset
+model.train(data='imagewoof160', epochs=100, imgsz=224)
+```
+
+It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.
+
+## Sample Images and Annotations
+
+The ImageWoof dataset contains colorful images of various dog breeds, providing a challenging dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239357533-ec833254-4351-491b-8cb3-59578ea5d0b2.png)
+
+The example showcases the subtle differences and similarities among the different dog breeds in the ImageWoof dataset, highlighting the complexity and difficulty of the classification task.
+
+## Citations and Acknowledgments
+
+If you use the ImageWoof dataset in your research or development work, please make sure to acknowledge the creators of the dataset by linking to the [official dataset repository](https://github.com/fastai/imagenette).
+
+We would like to acknowledge the FastAI team for creating and maintaining the ImageWoof dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageWoof dataset, visit the [ImageWoof dataset repository](https://github.com/fastai/imagenette).

yolov8-to/docs/datasets/classify/index.md

@@ -0,0 +1,120 @@
+---
+comments: true
+description: Explore image classification datasets supported by Ultralytics, learn the standard dataset format, and set up your own dataset for training models.
+keywords: Ultralytics, image classification, dataset, machine learning, CIFAR-10, ImageNet, MNIST, torchvision
+---
+
+# Image Classification Datasets Overview
+
+## Dataset format
+
+The folder structure for classification datasets in torchvision typically follows a standard format:
+
+```
+root/
+|-- class1/
+|   |-- img1.jpg
+|   |-- img2.jpg
+|   |-- ...
+|
+|-- class2/
+|   |-- img1.jpg
+|   |-- img2.jpg
+|   |-- ...
+|
+|-- class3/
+|   |-- img1.jpg
+|   |-- img2.jpg
+|   |-- ...
+|
+|-- ...
+```
+
+In this folder structure, the `root` directory contains one subdirectory for each class in the dataset. Each subdirectory is named after the corresponding class and contains all the images for that class. Each image file is named uniquely and is typically in a common image file format such as JPEG or PNG.
+
+** Example **
+
+For example, in the CIFAR10 dataset, the folder structure would look like this:
+
+```
+cifar-10-/
+|
+|-- train/
+|   |-- airplane/
+|   |   |-- 10008_airplane.png
+|   |   |-- 10009_airplane.png
+|   |   |-- ...
+|   |
+|   |-- automobile/
+|   |   |-- 1000_automobile.png
+|   |   |-- 1001_automobile.png
+|   |   |-- ...
+|   |
+|   |-- bird/
+|   |   |-- 10014_bird.png
+|   |   |-- 10015_bird.png
+|   |   |-- ...
+|   |
+|   |-- ...
+|
+|-- test/
+|   |-- airplane/
+|   |   |-- 10_airplane.png
+|   |   |-- 11_airplane.png
+|   |   |-- ...
+|   |
+|   |-- automobile/
+|   |   |-- 100_automobile.png
+|   |   |-- 101_automobile.png
+|   |   |-- ...
+|   |
+|   |-- bird/
+|   |   |-- 1000_bird.png
+|   |   |-- 1001_bird.png
+|   |   |-- ...
+|   |
+|   |-- ...
+```
+
+In this example, the `train` directory contains subdirectories for each class in the dataset, and each class subdirectory contains all the images for that class. The `test` directory has a similar structure. The `root` directory also contains other files that are part of the CIFAR10 dataset.
+
+## Usage
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='path/to/dataset', epochs=100, imgsz=640)
+        ```
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640
+        ```
+
+## Supported Datasets
+
+Ultralytics supports the following datasets with automatic download:
+
+* [Caltech 101](caltech101.md): A dataset containing images of 101 object categories for image classification tasks.
+* [Caltech 256](caltech256.md): An extended version of Caltech 101 with 256 object categories and more challenging images.
+* [CIFAR-10](cifar10.md): A dataset of 60K 32x32 color images in 10 classes, with 6K images per class.
+* [CIFAR-100](cifar100.md): An extended version of CIFAR-10 with 100 object categories and 600 images per class.
+* [Fashion-MNIST](fashion-mnist.md): A dataset consisting of 70,000 grayscale images of 10 fashion categories for image classification tasks.
+* [ImageNet](imagenet.md): A large-scale dataset for object detection and image classification with over 14 million images and 20,000 categories.
+* [ImageNet-10](imagenet10.md): A smaller subset of ImageNet with 10 categories for faster experimentation and testing.
+* [Imagenette](imagenette.md): A smaller subset of ImageNet that contains 10 easily distinguishable classes for quicker training and testing.
+* [Imagewoof](imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
+* [MNIST](mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
+
+### Adding your own dataset
+
+If you have your own dataset and would like to use it for training classification models with Ultralytics, ensure that it follows the format specified above under "Dataset format" and then point your `data` argument to the dataset directory.

yolov8-to/docs/datasets/classify/mnist.md

@@ -0,0 +1,86 @@
+---
+comments: true
+description: Detailed guide on the MNIST Dataset, a benchmark in the machine learning community for image classification tasks. Learn about its structure, usage and application.
+keywords: MNIST dataset, Ultralytics, image classification, machine learning, computer vision, deep learning, AI, dataset guide
+---
+
+# MNIST Dataset
+
+The [MNIST](http://yann.lecun.com/exdb/mnist/) (Modified National Institute of Standards and Technology) dataset is a large database of handwritten digits that is commonly used for training various image processing systems and machine learning models. It was created by "re-mixing" the samples from NIST's original datasets and has become a benchmark for evaluating the performance of image classification algorithms.
+
+## Key Features
+
+- MNIST contains 60,000 training images and 10,000 testing images of handwritten digits.
+- The dataset comprises grayscale images of size 28x28 pixels.
+- The images are normalized to fit into a 28x28 pixel bounding box and anti-aliased, introducing grayscale levels.
+- MNIST is widely used for training and testing in the field of machine learning, especially for image classification tasks.
+
+## Dataset Structure
+
+The MNIST dataset is split into two subsets:
+
+1. **Training Set**: This subset contains 60,000 images of handwritten digits used for training machine learning models.
+2. **Testing Set**: This subset consists of 10,000 images used for testing and benchmarking the trained models.
+
+## Extended MNIST (EMNIST)
+
+Extended MNIST (EMNIST) is a newer dataset developed and released by NIST to be the successor to MNIST. While MNIST included images only of handwritten digits, EMNIST includes all the images from NIST Special Database 19, which is a large database of handwritten uppercase and lowercase letters as well as digits. The images in EMNIST were converted into the same 28x28 pixel format, by the same process, as were the MNIST images. Accordingly, tools that work with the older, smaller MNIST dataset will likely work unmodified with EMNIST.
+
+## Applications
+
+The MNIST dataset is widely used for training and evaluating deep learning models in image classification tasks, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and various other machine learning algorithms. The dataset's simple and well-structured format makes it an essential resource for researchers and practitioners in the field of machine learning and computer vision.
+
+## Usage
+
+To train a CNN model on the MNIST dataset for 100 epochs with an image size of 32x32, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='mnist', epochs=100, imgsz=32)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        cnn detect train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28
+        ```
+
+## Sample Images and Annotations
+
+The MNIST dataset contains grayscale images of handwritten digits, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
+
+![Dataset sample image](https://upload.wikimedia.org/wikipedia/commons/2/27/MnistExamples.png)
+
+The example showcases the variety and complexity of the handwritten digits in the MNIST dataset, highlighting the importance of a diverse dataset for training robust image classification models.
+
+## Citations and Acknowledgments
+
+If you use the MNIST dataset in your
+
+research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{lecun2010mnist,
+                 title={MNIST handwritten digit database},
+                 author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
+                 journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
+                 volume={2},
+                 year={2010}
+        }
+        ```
+
+We would like to acknowledge Yann LeCun, Corinna Cortes, and Christopher J.C. Burges for creating and maintaining the MNIST dataset as a valuable resource for the machine learning and computer vision research community. For more information about the MNIST dataset and its creators, visit the [MNIST dataset website](http://yann.lecun.com/exdb/mnist/).

yolov8-to/docs/datasets/detect/argoverse.md

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+---
+comments: true
+description: Explore Argoverse, a comprehensive dataset for autonomous driving tasks including 3D tracking, motion forecasting and depth estimation used in YOLO.
+keywords: Argoverse dataset, autonomous driving, YOLO, 3D tracking, motion forecasting, LiDAR data, HD maps, ultralytics documentation
+---
+
+# Argoverse Dataset
+
+The [Argoverse](https://www.argoverse.org/) dataset is a collection of data designed to support research in autonomous driving tasks, such as 3D tracking, motion forecasting, and stereo depth estimation. Developed by Argo AI, the dataset provides a wide range of high-quality sensor data, including high-resolution images, LiDAR point clouds, and map data.
+
+!!! note
+
+    The Argoverse dataset *.zip file required for training was removed from Amazon S3 after the shutdown of Argo AI by Ford, but we have made it available for manual download on [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).
+
+## Key Features
+
+- Argoverse contains over 290K labeled 3D object tracks and 5 million object instances across 1,263 distinct scenes.
+- The dataset includes high-resolution camera images, LiDAR point clouds, and richly annotated HD maps.
+- Annotations include 3D bounding boxes for objects, object tracks, and trajectory information.
+- Argoverse provides multiple subsets for different tasks, such as 3D tracking, motion forecasting, and stereo depth estimation.
+
+## Dataset Structure
+
+The Argoverse dataset is organized into three main subsets:
+
+1. **Argoverse 3D Tracking**: This subset contains 113 scenes with over 290K labeled 3D object tracks, focusing on 3D object tracking tasks. It includes LiDAR point clouds, camera images, and sensor calibration information.
+2. **Argoverse Motion Forecasting**: This subset consists of 324K vehicle trajectories collected from 60 hours of driving data, suitable for motion forecasting tasks.
+3. **Argoverse Stereo Depth Estimation**: This subset is designed for stereo depth estimation tasks and includes over 10K stereo image pairs with corresponding LiDAR point clouds for ground truth depth estimation.
+
+## Applications
+
+The Argoverse dataset is widely used for training and evaluating deep learning models in autonomous driving tasks such as 3D object tracking, motion forecasting, and stereo depth estimation. The dataset's diverse set of sensor data, object annotations, and map information make it a valuable resource for researchers and practitioners in the field of autonomous driving.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Argoverse dataset, the `Argoverse.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Argoverse.yaml).
+
+!!! example "ultralytics/cfg/datasets/Argoverse.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/Argoverse.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='Argoverse.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=Argoverse.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+The Argoverse dataset contains a diverse set of sensor data, including camera images, LiDAR point clouds, and HD map information, providing rich context for autonomous driving tasks. Here are some examples of data from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://www.argoverse.org/assets/images/reference_images/av2_ground_height.png)
+
+- **Argoverse 3D Tracking**: This image demonstrates an example of 3D object tracking, where objects are annotated with 3D bounding boxes. The dataset provides LiDAR point clouds and camera images to facilitate the development of models for this task.
+
+The example showcases the variety and complexity of the data in the Argoverse dataset and highlights the importance of high-quality sensor data for autonomous driving tasks.
+
+## Citations and Acknowledgments
+
+If you use the Argoverse dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @inproceedings{chang2019argoverse,
+          title={Argoverse: 3D Tracking and Forecasting with Rich Maps},
+          author={Chang, Ming-Fang and Lambert, John and Sangkloy, Patsorn and Singh, Jagjeet and Bak, Slawomir and Hartnett, Andrew and Wang, Dequan and Carr, Peter and Lucey, Simon and Ramanan, Deva and others},
+          booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+          pages={8748--8757},
+          year={2019}
+        }
+        ```
+
+We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).

yolov8-to/docs/datasets/detect/coco.md

@@ -0,0 +1,94 @@
+---
+comments: true
+description: Learn how COCO, a leading dataset for object detection and segmentation, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+keywords: Ultralytics, COCO dataset, object detection, YOLO, YOLO model training, image segmentation, computer vision, deep learning models
+---
+
+# COCO Dataset
+
+The [COCO](https://cocodataset.org/#home) (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.
+
+## Key Features
+
+- COCO contains 330K images, with 200K images having annotations for object detection, segmentation, and captioning tasks.
+- The dataset comprises 80 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as umbrellas, handbags, and sports equipment.
+- Annotations include object bounding boxes, segmentation masks, and captions for each image.
+- COCO provides standardized evaluation metrics like mean Average Precision (mAP) for object detection, and mean Average Recall (mAR) for segmentation tasks, making it suitable for comparing model performance.
+
+## Dataset Structure
+
+The COCO dataset is split into three subsets:
+
+1. **Train2017**: This subset contains 118K images for training object detection, segmentation, and captioning models.
+2. **Val2017**: This subset has 5K images used for validation purposes during model training.
+3. **Test2017**: This subset consists of 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
+
+## Applications
+
+The COCO dataset is widely used for training and evaluating deep learning models in object detection (such as YOLO, Faster R-CNN, and SSD), instance segmentation (such as Mask R-CNN), and keypoint detection (such as OpenPose). The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
+
+!!! example "ultralytics/cfg/datasets/coco.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+The COCO dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/236811818-5b566576-1e92-42fa-9462-4b6a848abe89.jpg)
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the COCO dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the COCO dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

yolov8-to/docs/datasets/detect/coco8.md

@@ -0,0 +1,84 @@
+---
+comments: true
+description: Discover the benefits of using the practical and diverse COCO8 dataset for object detection model testing. Learn to configure and use it via Ultralytics HUB and YOLOv8.
+keywords: Ultralytics, COCO8 dataset, object detection, model testing, dataset configuration, detection approaches, sanity check, training pipelines, YOLOv8
+---
+
+# COCO8 Dataset
+
+## 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.
+
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
+and [YOLOv8](https://github.com/ultralytics/ultralytics).
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8 dataset, the `coco8.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
+
+!!! example "ultralytics/cfg/datasets/coco8.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco8.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+Here are some examples of images from the COCO8 dataset, along with their corresponding annotations:
+
+<img src="https://user-images.githubusercontent.com/26833433/236818348-e6260a3d-0454-436b-83a9-de366ba07235.jpg" alt="Dataset sample image" width="800">
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the COCO8 dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the COCO dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

yolov8-to/docs/datasets/detect/globalwheat2020.md

@@ -0,0 +1,91 @@
+---
+comments: true
+description: Understand how to utilize the vast Global Wheat Head Dataset for building wheat head detection models. Features, structure, applications, usage, sample data, and citation.
+keywords: Ultralytics, YOLO, Global Wheat Head Dataset, wheat head detection, plant phenotyping, crop management, deep learning, outdoor images, annotations, YAML configuration
+---
+
+# Global Wheat Head Dataset
+
+The [Global Wheat Head Dataset](http://www.global-wheat.com/) is a collection of images designed to support the development of accurate wheat head detection models for applications in wheat phenotyping and crop management. Wheat heads, also known as spikes, are the grain-bearing parts of the wheat plant. Accurate estimation of wheat head density and size is essential for assessing crop health, maturity, and yield potential. The dataset, created by a collaboration of nine research institutes from seven countries, covers multiple growing regions to ensure models generalize well across different environments.
+
+## Key Features
+
+- The dataset contains over 3,000 training images from Europe (France, UK, Switzerland) and North America (Canada).
+- It includes approximately 1,000 test images from Australia, Japan, and China.
+- Images are outdoor field images, capturing the natural variability in wheat head appearances.
+- Annotations include wheat head bounding boxes to support object detection tasks.
+
+## Dataset Structure
+
+The Global Wheat Head Dataset is organized into two main subsets:
+
+1. **Training Set**: This subset contains over 3,000 images from Europe and North America. The images are labeled with wheat head bounding boxes, providing ground truth for training object detection models.
+2. **Test Set**: This subset consists of approximately 1,000 images from Australia, Japan, and China. These images are used for evaluating the performance of trained models on unseen genotypes, environments, and observational conditions.
+
+## Applications
+
+The Global Wheat Head Dataset is widely used for training and evaluating deep learning models in wheat head detection tasks. The dataset's diverse set of images, capturing a wide range of appearances, environments, and conditions, make it a valuable resource for researchers and practitioners in the field of plant phenotyping and crop management.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Global Wheat Head Dataset, the `GlobalWheat2020.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/GlobalWheat2020.yaml).
+
+!!! example "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/GlobalWheat2020.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=GlobalWheat2020.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+The Global Wheat Head Dataset contains a diverse set of outdoor field images, capturing the natural variability in wheat head appearances, environments, and conditions. Here are some examples of data from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://i.ytimg.com/vi/yqvMuw-uedU/maxresdefault.jpg)
+
+- **Wheat Head Detection**: This image demonstrates an example of wheat head detection, where wheat heads are annotated with bounding boxes. The dataset provides a variety of images to facilitate the development of models for this task.
+
+The example showcases the variety and complexity of the data in the Global Wheat Head Dataset and highlights the importance of accurate wheat head detection for applications in wheat phenotyping and crop management.
+
+## Citations and Acknowledgments
+
+If you use the Global Wheat Head Dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{david2020global,
+                 title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
+                 author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
+                 journal={arXiv preprint arXiv:2005.02162},
+                 year={2020}
+        }
+        ```
+
+We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).

yolov8-to/docs/datasets/detect/index.md

@@ -0,0 +1,108 @@
+---
+comments: true
+description: Navigate through supported dataset formats, methods to utilize them and how to add your own datasets. Get insights on porting or converting label formats.
+keywords: Ultralytics, YOLO, datasets, object detection, dataset formats, label formats, data conversion
+---
+
+# Object Detection Datasets Overview
+
+Training a robust and accurate object detection model requires a comprehensive dataset. This guide introduces various formats of datasets that are compatible with the Ultralytics YOLO model and provides insights into their structure, usage, and how to convert between different formats.
+
+## Supported Dataset Formats
+
+### Ultralytics YOLO format
+
+The Ultralytics YOLO format is a dataset configuration format that allows you to define the dataset root directory, the relative paths to training/validation/testing image directories or *.txt files containing image paths, and a dictionary of class names. Here is an example:
+
+```yaml
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+test:  # test images (optional)
+
+# Classes (80 COCO classes)
+names:
+  0: person
+  1: bicycle
+  2: car
+  ...
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+```
+
+Labels for this format should be exported to YOLO format with one `*.txt` file per image. If there are no objects in an image, no `*.txt` file is required. The `*.txt` file should be formatted with one row per object in `class x_center y_center width height` format. Box coordinates must be in **normalized xywh** format (from 0 to 1). If your boxes are in pixels, you should divide `x_center` and `width` by image width, and `y_center` and `height` by image height. Class numbers should be zero-indexed (start with 0).
+
+<p align="center"><img width="750" src="https://user-images.githubusercontent.com/26833433/91506361-c7965000-e886-11ea-8291-c72b98c25eec.jpg"></p>
+
+The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`):
+
+<p align="center"><img width="428" src="https://user-images.githubusercontent.com/26833433/112467037-d2568c00-8d66-11eb-8796-55402ac0d62f.png"></p>
+
+When using the Ultralytics YOLO format, organize your training and validation images and labels as shown in the example below.
+
+<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/134436012-65111ad1-9541-4853-81a6-f19a3468b75f.png"></p>
+
+## Usage
+
+Here's how you can use these formats to train your model:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        ```
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Supported Datasets
+
+Here is a list of the supported datasets and a brief description for each:
+
+- [**Argoverse**](./argoverse.md): A collection of sensor data collected from autonomous vehicles. It contains 3D tracking annotations for car objects.
+- [**COCO**](./coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
+- [**COCO8**](./coco8.md): A smaller subset of the COCO dataset, COCO8 is more lightweight and faster to train.
+- [**GlobalWheat2020**](./globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
+- [**Objects365**](./objects365.md): A large-scale object detection dataset with 365 object categories and 600k images, aimed at advancing object detection research.
+- [**OpenImagesV7**](./open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
+- [**SKU-110K**](./sku-110k.md): A dataset containing images of densely packed retail products, intended for retail environment object detection.
+- [**VisDrone**](./visdrone.md): A dataset focusing on drone-based images, containing various object categories like cars, pedestrians, and cyclists.
+- [**VOC**](./voc.md): PASCAL VOC is a popular object detection dataset with 20 object categories including vehicles, animals, and furniture.
+- [**xView**](./xview.md): A dataset containing high-resolution satellite imagery, designed for the detection of various object classes in overhead views.
+
+### Adding your own dataset
+
+If you have your own dataset and would like to use it for training detection models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.
+
+## Port or Convert Label Formats
+
+### COCO Dataset Format to YOLO Format
+
+You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics.data.converter import convert_coco
+        
+        convert_coco(labels_dir='path/to/coco/annotations/')
+        ```
+
+This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
+
+Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.

yolov8-to/docs/datasets/detect/objects365.md

@@ -0,0 +1,92 @@
+---
+comments: true
+description: Discover the Objects365 dataset, a wide-scale, high-quality resource for object detection research. Learn to use it with the Ultralytics YOLO model.
+keywords: Objects365, object detection, Ultralytics, dataset, YOLO, bounding boxes, annotations, computer vision, deep learning, training models
+---
+
+# Objects365 Dataset
+
+The [Objects365](https://www.objects365.org/) dataset is a large-scale, high-quality dataset designed to foster object detection research with a focus on diverse objects in the wild. Created by a team of [Megvii](https://en.megvii.com/) researchers, the dataset offers a wide range of high-resolution images with a comprehensive set of annotated bounding boxes covering 365 object categories.
+
+## Key Features
+
+- Objects365 contains 365 object categories, with 2 million images and over 30 million bounding boxes.
+- The dataset includes diverse objects in various scenarios, providing a rich and challenging benchmark for object detection tasks.
+- Annotations include bounding boxes for objects, making it suitable for training and evaluating object detection models.
+- Objects365 pre-trained models significantly outperform ImageNet pre-trained models, leading to better generalization on various tasks.
+
+## Dataset Structure
+
+The Objects365 dataset is organized into a single set of images with corresponding annotations:
+
+- **Images**: The dataset includes 2 million high-resolution images, each containing a variety of objects across 365 categories.
+- **Annotations**: The images are annotated with over 30 million bounding boxes, providing comprehensive ground truth information for object detection tasks.
+
+## Applications
+
+The Objects365 dataset is widely used for training and evaluating deep learning models in object detection tasks. The dataset's diverse set of object categories and high-quality annotations make it a valuable resource for researchers and practitioners in the field of computer vision.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the Objects365 Dataset, the `Objects365.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml).
+
+!!! example "ultralytics/cfg/datasets/Objects365.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/Objects365.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='Objects365.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+The Objects365 dataset contains a diverse set of high-resolution images with objects from 365 categories, providing rich context for object detection tasks. Here are some examples of the images in the dataset:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/238215467-caf757dd-0b87-4b0d-bb19-d94a547f7fbf.jpg)
+
+- **Objects365**: This image demonstrates an example of object detection, where objects are annotated with bounding boxes. The dataset provides a wide range of images to facilitate the development of models for this task.
+
+The example showcases the variety and complexity of the data in the Objects365 dataset and highlights the importance of accurate object detection for computer vision applications.
+
+## Citations and Acknowledgments
+
+If you use the Objects365 dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @inproceedings{shao2019objects365,
+          title={Objects365: A Large-scale, High-quality Dataset for Object Detection},
+          author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Li, Jing and Zhang, Xiangyu and Sun, Jian},
+          booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+          pages={8425--8434},
+          year={2019}
+        }
+        ```
+
+We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).

yolov8-to/docs/datasets/detect/open-images-v7.md

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+---
+comments: true
+description: Dive into Google's Open Images V7, a comprehensive dataset offering a broad scope for computer vision research. Understand its usage with deep learning models.
+keywords: Open Images V7, object detection, segmentation masks, visual relationships, localized narratives, computer vision, deep learning, annotations, bounding boxes
+---
+
+# Open Images V7 Dataset
+
+[Open Images V7](https://storage.googleapis.com/openimages/web/index.html) is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of computer vision, it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
+
+![Open Images V7 classes visual](https://user-images.githubusercontent.com/26833433/258660358-2dc07771-ec08-4d11-b24a-f66e07550050.png)
+
+## Key Features
+
+- Encompasses ~9M images annotated in various ways to suit multiple computer vision tasks.
+- Houses a staggering 16M bounding boxes across 600 object classes in 1.9M images. These boxes are primarily hand-drawn by experts ensuring high precision.
+- Visual relationship annotations totaling 3.3M are available, detailing 1,466 unique relationship triplets, object properties, and human activities.
+- V5 introduced segmentation masks for 2.8M objects across 350 classes.
+- V6 introduced 675k localized narratives that amalgamate voice, text, and mouse traces highlighting described objects.
+- V7 introduced 66.4M point-level labels on 1.4M images, spanning 5,827 classes.
+- Encompasses 61.4M image-level labels across a diverse set of 20,638 classes.
+- Provides a unified platform for image classification, object detection, relationship detection, instance segmentation, and multimodal image descriptions.
+
+## Dataset Structure
+
+Open Images V7 is structured in multiple components catering to varied computer vision challenges:
+
+- **Images**: About 9 million images, often showcasing intricate scenes with an average of 8.3 objects per image.
+- **Bounding Boxes**: Over 16 million boxes that demarcate objects across 600 categories.
+- **Segmentation Masks**: These detail the exact boundary of 2.8M objects across 350 classes.
+- **Visual Relationships**: 3.3M annotations indicating object relationships, properties, and actions.
+- **Localized Narratives**: 675k descriptions combining voice, text, and mouse traces.
+- **Point-Level Labels**: 66.4M labels across 1.4M images, suitable for zero/few-shot semantic segmentation.
+
+## Applications
+
+Open Images V7 is a cornerstone for training and evaluating state-of-the-art models in various computer vision tasks. The dataset's broad scope and high-quality annotations make it indispensable for researchers and developers specializing in computer vision.
+
+## Dataset YAML
+
+Typically, datasets come with a YAML (Yet Another Markup Language) file that delineates the dataset's configuration. For the case of Open Images V7, a hypothetical `OpenImagesV7.yaml` might exist. For accurate paths and configurations, one should refer to the dataset's official repository or documentation.
+
+!!! example "OpenImagesV7.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/open-images-v7.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! warning
+
+    The complete Open Images V7 dataset comprises 1,743,042 training images and 41,620 validation images, requiring approximately **561 GB of storage space** upon download.
+    
+    Executing the commands provided below will trigger an automatic download of the full dataset if it's not already present locally. Before running the below example it's crucial to:
+    
+    - Verify that your device has enough storage capacity.
+    - Ensure a robust and speedy internet connection.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a COCO-pretrained YOLOv8n model
+        model = YOLO('yolov8n.pt')
+
+        # Train the model on the Open Images V7 dataset
+        results = model.train(data='open-images-v7.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Train a COCO-pretrained YOLOv8n model on the Open Images V7 dataset
+        yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+Illustrations of the dataset help provide insights into its richness:
+
+![Dataset sample image](https://storage.googleapis.com/openimages/web/images/oidv7_all-in-one_example_ab.jpg)
+
+- **Open Images V7**: This image exemplifies the depth and detail of annotations available, including bounding boxes, relationships, and segmentation masks.
+
+Researchers can gain invaluable insights into the array of computer vision challenges that the dataset addresses, from basic object detection to intricate relationship identification.
+
+## Citations and Acknowledgments
+
+For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{OpenImages,
+          author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
+          title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
+          year = {2020},
+          journal = {IJCV}
+        }
+        ```
+
+A heartfelt acknowledgment goes out to the Google AI team for creating and maintaining the Open Images V7 dataset. For a deep dive into the dataset and its offerings, navigate to the [official Open Images V7 website](https://storage.googleapis.com/openimages/web/index.html).

yolov8-to/docs/datasets/detect/sku-110k.md

@@ -0,0 +1,93 @@
+---
+comments: true
+description: 'Explore the SKU-110k dataset: densely packed retail shelf images for object detection research. Learn how to use it with Ultralytics.'
+keywords: SKU-110k dataset, object detection, retail shelf images, Ultralytics, YOLO, computer vision, deep learning models
+---
+
+# SKU-110k Dataset
+
+The [SKU-110k](https://github.com/eg4000/SKU110K_CVPR19) dataset is a collection of densely packed retail shelf images, designed to support research in object detection tasks. Developed by Eran Goldman et al., the dataset contains over 110,000 unique store keeping unit (SKU) categories with densely packed objects, often looking similar or even identical, positioned in close proximity.
+
+![Dataset sample image](https://github.com/eg4000/SKU110K_CVPR19/raw/master/figures/benchmarks_comparison.jpg)
+
+## Key Features
+
+- SKU-110k contains images of store shelves from around the world, featuring densely packed objects that pose challenges for state-of-the-art object detectors.
+- The dataset includes over 110,000 unique SKU categories, providing a diverse range of object appearances.
+- Annotations include bounding boxes for objects and SKU category labels.
+
+## Dataset Structure
+
+The SKU-110k dataset is organized into three main subsets:
+
+1. **Training set**: This subset contains images and annotations used for training object detection models.
+2. **Validation set**: This subset consists of images and annotations used for model validation during training.
+3. **Test set**: This subset is designed for the final evaluation of trained object detection models.
+
+## Applications
+
+The SKU-110k dataset is widely used for training and evaluating deep learning models in object detection tasks, especially in densely packed scenes such as retail shelf displays. The dataset's diverse set of SKU categories and densely packed object arrangements make it a valuable resource for researchers and practitioners in the field of computer vision.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the case of the SKU-110K dataset, the `SKU-110K.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/SKU-110K.yaml).
+
+!!! example "ultralytics/cfg/datasets/SKU-110K.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/SKU-110K.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=SKU-110K.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+The SKU-110k dataset contains a diverse set of retail shelf images with densely packed objects, providing rich context for object detection tasks. Here are some examples of data from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/238215979-1ab791c4-15d9-46f6-a5d6-0092c05dff7a.jpg)
+
+- **Densely packed retail shelf image**: This image demonstrates an example of densely packed objects in a retail shelf setting. Objects are annotated with bounding boxes and SKU category labels.
+
+The example showcases the variety and complexity of the data in the SKU-110k dataset and highlights the importance of high-quality data for object detection tasks.
+
+## Citations and Acknowledgments
+
+If you use the SKU-110k dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @inproceedings{goldman2019dense,
+         author    = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
+         title     = {Precise Detection in Densely Packed Scenes},
+         booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
+         year      = {2019}
+        }
+        ```
+
+We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).

yolov8-to/docs/datasets/detect/visdrone.md

@@ -0,0 +1,92 @@
+---
+comments: true
+description: Explore the VisDrone Dataset, a large-scale benchmark for drone-based image analysis, and learn how to train a YOLO model using it.
+keywords: VisDrone Dataset, Ultralytics, drone-based image analysis, YOLO model, object detection, object tracking, crowd counting
+---
+
+# VisDrone Dataset
+
+The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis.
+
+VisDrone is composed of 288 video clips with 261,908 frames and 10,209 static images, captured by various drone-mounted cameras. The dataset covers a wide range of aspects, including location (14 different cities across China), environment (urban and rural), objects (pedestrians, vehicles, bicycles, etc.), and density (sparse and crowded scenes). The dataset was collected using various drone platforms under different scenarios and weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets such as pedestrians, cars, bicycles, and tricycles. Attributes like scene visibility, object class, and occlusion are also provided for better data utilization.
+
+## Dataset Structure
+
+The VisDrone dataset is organized into five main subsets, each focusing on a specific task:
+
+1. **Task 1**: Object detection in images
+2. **Task 2**: Object detection in videos
+3. **Task 3**: Single-object tracking
+4. **Task 4**: Multi-object tracking
+5. **Task 5**: Crowd counting
+
+## Applications
+
+The VisDrone dataset is widely used for training and evaluating deep learning models in drone-based computer vision tasks such as object detection, object tracking, and crowd counting. The dataset's diverse set of sensor data, object annotations, and attributes make it a valuable resource for researchers and practitioners in the field of drone-based computer vision.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Visdrone dataset, the `VisDrone.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
+
+!!! example "ultralytics/cfg/datasets/VisDrone.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/VisDrone.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='VisDrone.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+The VisDrone dataset contains a diverse set of images and videos captured by drone-mounted cameras. Here are some examples of data from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/238217600-df0b7334-4c9e-4c77-81a5-c70cd33429cc.jpg)
+
+- **Task 1**: Object detection in images - This image demonstrates an example of object detection in images, where objects are annotated with bounding boxes. The dataset provides a wide variety of images taken from different locations, environments, and densities to facilitate the development of models for this task.
+
+The example showcases the variety and complexity of the data in the VisDrone dataset and highlights the importance of high-quality sensor data for drone-based computer vision tasks.
+
+## Citations and Acknowledgments
+
+If you use the VisDrone dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @ARTICLE{9573394,
+          author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+          journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+          title={Detection and Tracking Meet Drones Challenge},
+          year={2021},
+          volume={},
+          number={},
+          pages={1-1},
+          doi={10.1109/TPAMI.2021.3119563}}
+        ```
+
+We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).

yolov8-to/docs/datasets/detect/voc.md

@@ -0,0 +1,95 @@
+---
+comments: true
+description: A complete guide to the PASCAL VOC dataset used for object detection, segmentation and classification tasks with relevance to YOLO model training.
+keywords: Ultralytics, PASCAL VOC dataset, object detection, segmentation, image classification, YOLO, model training, VOC.yaml, deep learning
+---
+
+# VOC Dataset
+
+The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a well-known object detection, segmentation, and classification dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and classification tasks.
+
+## Key Features
+
+- VOC dataset includes two main challenges: VOC2007 and VOC2012.
+- The dataset comprises 20 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as boats, sofas, and dining tables.
+- Annotations include object bounding boxes and class labels for object detection and classification tasks, and segmentation masks for the segmentation tasks.
+- VOC provides standardized evaluation metrics like mean Average Precision (mAP) for object detection and classification, making it suitable for comparing model performance.
+
+## Dataset Structure
+
+The VOC dataset is split into three subsets:
+
+1. **Train**: This subset contains images for training object detection, segmentation, and classification models.
+2. **Validation**: This subset has images used for validation purposes during model training.
+3. **Test**: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [PASCAL VOC evaluation server](http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php) for performance evaluation.
+
+## Applications
+
+The VOC dataset is widely used for training and evaluating deep learning models in object detection (such as YOLO, Faster R-CNN, and SSD), instance segmentation (such as Mask R-CNN), and image classification. The dataset's diverse set of object categories, large number of annotated images, and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the VOC dataset, the `VOC.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VOC.yaml).
+
+!!! example "ultralytics/cfg/datasets/VOC.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/VOC.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='VOC.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from
+        a pretrained *.pt model
+        yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+The VOC dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://github.com/ultralytics/ultralytics/assets/26833433/7d4c18f4-774e-43f8-a5f3-9467cda7de4a)
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the VOC dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the VOC dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{everingham2010pascal,
+              title={The PASCAL Visual Object Classes (VOC) Challenge},
+              author={Mark Everingham and Luc Van Gool and Christopher K. I. Williams and John Winn and Andrew Zisserman},
+              year={2010},
+              eprint={0909.5206},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).

yolov8-to/docs/datasets/detect/xview.md

@@ -0,0 +1,97 @@
+---
+comments: true
+description: Explore xView, a large-scale, high resolution satellite imagery dataset for object detection. Dive into dataset structure, usage examples & its potential applications.
+keywords: Ultralytics, YOLO, computer vision, xView dataset, satellite imagery, object detection, overhead imagery, training, deep learning, dataset YAML
+---
+
+# xView Dataset
+
+The [xView](http://xviewdataset.org/) dataset is one of the largest publicly available datasets of overhead imagery, containing images from complex scenes around the world annotated using bounding boxes. The goal of the xView dataset is to accelerate progress in four computer vision frontiers:
+
+1. Reduce minimum resolution for detection.
+2. Improve learning efficiency.
+3. Enable discovery of more object classes.
+4. Improve detection of fine-grained classes.
+
+xView builds on the success of challenges like Common Objects in Context (COCO) and aims to leverage computer vision to analyze the growing amount of available imagery from space in order to understand the visual world in new ways and address a range of important applications.
+
+## Key Features
+
+- xView contains over 1 million object instances across 60 classes.
+- The dataset has a resolution of 0.3 meters, providing higher resolution imagery than most public satellite imagery datasets.
+- xView features a diverse collection of small, rare, fine-grained, and multi-type objects with bounding box annotation.
+- Comes with a pre-trained baseline model using the TensorFlow object detection API and an example for PyTorch.
+
+## Dataset Structure
+
+The xView dataset is composed of satellite images collected from WorldView-3 satellites at a 0.3m ground sample distance. It contains over 1 million objects across 60 classes in over 1,400 km² of imagery.
+
+## Applications
+
+The xView dataset is widely used for training and evaluating deep learning models for object detection in overhead imagery. The dataset's diverse set of object classes and high-resolution imagery make it a valuable resource for researchers and practitioners in the field of computer vision, especially for satellite imagery analysis.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the xView dataset, the `xView.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/xView.yaml).
+
+!!! example "ultralytics/cfg/datasets/xView.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/xView.yaml"
+    ```
+
+## Usage
+
+To train a model on the xView dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='xView.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=xView.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+The xView dataset contains high-resolution satellite images with a diverse set of objects annotated using bounding boxes. Here are some examples of data from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://github-production-user-asset-6210df.s3.amazonaws.com/26833433/238799379-bb3b02f0-dee4-4e67-80ae-4b2378b813ad.jpg)
+
+- **Overhead Imagery**: This image demonstrates an example of object detection in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task.
+
+The example showcases the variety and complexity of the data in the xView dataset and highlights the importance of high-quality satellite imagery for object detection tasks.
+
+## Citations and Acknowledgments
+
+If you use the xView dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lam2018xview,
+              title={xView: Objects in Context in Overhead Imagery},
+              author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
+              year={2018},
+              eprint={1802.07856},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).

yolov8-to/docs/datasets/index.md

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+---
+comments: true
+description: Explore various computer vision datasets supported by Ultralytics for object detection, segmentation, pose estimation, image classification, and multi-object tracking.
+keywords: computer vision, datasets, Ultralytics, YOLO, object detection, instance segmentation, pose estimation, image classification, multi-object tracking
+---
+
+# Datasets Overview
+
+Ultralytics provides support for various datasets to facilitate computer vision tasks such as detection, instance segmentation, pose estimation, classification, and multi-object tracking. Below is a list of the main Ultralytics datasets, followed by a summary of each computer vision task and the respective datasets.
+
+## [Detection Datasets](detect/index.md)
+
+Bounding box object detection is a computer vision technique that involves detecting and localizing objects in an image by drawing a bounding box around each object.
+
+- [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
+- [COCO](detect/coco.md): A large-scale dataset designed for object detection, segmentation, and captioning with over 200K labeled images.
+- [COCO8](detect/coco8.md): Contains the first 4 images from COCO train and COCO val, suitable for quick tests.
+- [Global Wheat 2020](detect/globalwheat2020.md): A dataset of wheat head images collected from around the world for object detection and localization tasks.
+- [Objects365](detect/objects365.md): A high-quality, large-scale dataset for object detection with 365 object categories and over 600K annotated images.
+- [OpenImagesV7](detect/open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
+- [SKU-110K](detect/sku-110k.md): A dataset featuring dense object detection in retail environments with over 11K images and 1.7 million bounding boxes.
+- [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
+- [VOC](detect/voc.md): The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
+- [xView](detect/xview.md): A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
+
+## [Instance Segmentation Datasets](segment/index.md)
+
+Instance segmentation is a computer vision technique that involves identifying and localizing objects in an image at the pixel level.
+
+- [COCO](segment/coco.md): A large-scale dataset designed for object detection, segmentation, and captioning tasks with over 200K labeled images.
+- [COCO8-seg](segment/coco8-seg.md): A smaller dataset for instance segmentation tasks, containing a subset of 8 COCO images with segmentation annotations.
+
+## [Pose Estimation](pose/index.md)
+
+Pose estimation is a technique used to determine the pose of the object relative to the camera or the world coordinate system.
+
+- [COCO](pose/coco.md): A large-scale dataset with human pose annotations designed for pose estimation tasks.
+- [COCO8-pose](pose/coco8-pose.md): A smaller dataset for pose estimation tasks, containing a subset of 8 COCO images with human pose annotations.
+
+## [Classification](classify/index.md)
+
+Image classification is a computer vision task that involves categorizing an image into one or more predefined classes or categories based on its visual content.
+
+- [Caltech 101](classify/caltech101.md): A dataset containing images of 101 object categories for image classification tasks.
+- [Caltech 256](classify/caltech256.md): An extended version of Caltech 101 with 256 object categories and more challenging images.
+- [CIFAR-10](classify/cifar10.md): A dataset of 60K 32x32 color images in 10 classes, with 6K images per class.
+- [CIFAR-100](classify/cifar100.md): An extended version of CIFAR-10 with 100 object categories and 600 images per class.
+- [Fashion-MNIST](classify/fashion-mnist.md): A dataset consisting of 70,000 grayscale images of 10 fashion categories for image classification tasks.
+- [ImageNet](classify/imagenet.md): A large-scale dataset for object detection and image classification with over 14 million images and 20,000 categories.
+- [ImageNet-10](classify/imagenet10.md): A smaller subset of ImageNet with 10 categories for faster experimentation and testing.
+- [Imagenette](classify/imagenette.md): A smaller subset of ImageNet that contains 10 easily distinguishable classes for quicker training and testing.
+- [Imagewoof](classify/imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
+- [MNIST](classify/mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
+
+## [Oriented Bounding Boxes (OBB)](obb/index.md)
+
+Oriented Bounding Boxes (OBB) is a method in computer vision for detecting angled objects in images using rotated bounding boxes, often applied to aerial and satellite imagery.
+
+- [DOTAv2](obb/dota-v2.md): A popular OBB aerial imagery dataset with 1.7 million instances and 11,268 images.
+
+## [Multi-Object Tracking](track/index.md)
+
+Multi-object tracking is a computer vision technique that involves detecting and tracking multiple objects over time in a video sequence.
+
+- [Argoverse](detect/argoverse.md): A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations for multi-object tracking tasks.
+- [VisDrone](detect/visdrone.md): A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.

yolov8-to/docs/datasets/obb/dota-v2.md

@@ -0,0 +1,129 @@
+---
+comments: true
+description: Delve into DOTA v2, an Oriented Bounding Box (OBB) aerial imagery dataset with 1.7 million instances and 11,268 images.
+keywords: DOTA v2, object detection, aerial images, computer vision, deep learning, annotations, oriented bounding boxes, OBB
+---
+
+# DOTA v2 Dataset with OBB
+
+[DOTA v2](https://captain-whu.github.io/DOTA/index.html) stands as a specialized dataset, emphasizing object detection in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with Oriented Bounding Boxes (OBB).
+
+![DOTA v2 classes visual](https://user-images.githubusercontent.com/26833433/259461765-72fdd0d8-266b-44a9-8199-199329bf5ca9.jpg)
+
+## Key Features
+
+- Collection from various sensors and platforms, with image sizes ranging from 800 × 800 to 20,000 × 20,000 pixels.
+- Features more than 1.7M Oriented Bounding Boxes across 18 categories.
+- Encompasses multiscale object detection.
+- Instances are annotated by experts using arbitrary (8 d.o.f.) quadrilateral, capturing objects of different scales, orientations, and shapes.
+
+## Dataset Versions
+
+### DOTA-v1.0
+
+- Contains 15 common categories.
+- Comprises 2,806 images with 188,282 instances.
+- Split ratios: 1/2 for training, 1/6 for validation, and 1/3 for testing.
+
+### DOTA-v1.5
+
+- Incorporates the same images as DOTA-v1.0.
+- Very small instances (less than 10 pixels) are also annotated.
+- Addition of a new category: "container crane".
+- A total of 403,318 instances.
+- Released for the DOAI Challenge 2019 on Object Detection in Aerial Images.
+
+### DOTA-v2.0
+
+- Collections from Google Earth, GF-2 Satellite, and other aerial images.
+- Contains 18 common categories.
+- Comprises 11,268 images with a whopping 1,793,658 instances.
+- New categories introduced: "airport" and "helipad".
+- Image splits:
+    - Training: 1,830 images with 268,627 instances.
+    - Validation: 593 images with 81,048 instances.
+    - Test-dev: 2,792 images with 353,346 instances.
+    - Test-challenge: 6,053 images with 1,090,637 instances.
+
+## Dataset Structure
+
+DOTA v2 exhibits a structured layout tailored for OBB object detection challenges:
+
+- **Images**: A vast collection of high-resolution aerial images capturing diverse terrains and structures.
+- **Oriented Bounding Boxes**: Annotations in the form of rotated rectangles encapsulating objects irrespective of their orientation, ideal for capturing objects like airplanes, ships, and buildings.
+
+## Applications
+
+DOTA v2 serves as a benchmark for training and evaluating models specifically tailored for aerial image analysis. With the inclusion of OBB annotations, it provides a unique challenge, enabling the development of specialized object detection models that cater to aerial imagery's nuances.
+
+## Dataset YAML
+
+Typically, datasets incorporate a YAML (Yet Another Markup Language) file detailing the dataset's configuration. For DOTA v2, a hypothetical `DOTAv2.yaml` could be used. For accurate paths and configurations, it's vital to consult the dataset's official repository or documentation.
+
+!!! example "DOTAv2.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/DOTAv2.yaml"
+    ```
+
+## Usage
+
+To train a model on the DOTA v2 dataset, you can utilize the following code snippets. Always refer to your model's documentation for a thorough list of available arguments.
+
+!!! warning
+
+    Please note that all images and associated annotations in the DOTAv2 dataset can be used for academic purposes, but commercial use is prohibited. Your understanding and respect for the dataset creators' wishes are greatly appreciated!
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Create a new YOLOv8n-OBB model from scratch
+        model = YOLO('yolov8n-obb.yaml')
+
+        # Train the model on the DOTAv2 dataset
+        results = model.train(data='DOTAv2.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Train a new YOLOv8n-OBB model on the DOTAv2 dataset
+        yolo detect train data=DOTAv2.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Data and Annotations
+
+Having a glance at the dataset illustrates its depth:
+
+![Dataset sample image](https://captain-whu.github.io/DOTA/images/instances-DOTA.jpg)
+
+- **DOTA v2**: This snapshot underlines the complexity of aerial scenes and the significance of Oriented Bounding Box annotations, capturing objects in their natural orientation.
+
+The dataset's richness offers invaluable insights into object detection challenges exclusive to aerial imagery.
+
+## Citations and Acknowledgments
+
+For those leveraging DOTA v2 in their endeavors, it's pertinent to cite the relevant research papers:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{9560031,
+          author={Ding, Jian and Xue, Nan and Xia, Gui-Song and Bai, Xiang and Yang, Wen and Yang, Michael and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
+          journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+          title={Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges},
+          year={2021},
+          volume={},
+          number={},
+          pages={1-1},
+          doi={10.1109/TPAMI.2021.3117983}
+        }
+        ```
+
+A special note of gratitude to the team behind DOTA v2 for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the [official DOTA v2 website](https://captain-whu.github.io/DOTA/index.html).

yolov8-to/docs/datasets/obb/index.md

@@ -0,0 +1,84 @@
+---
+comments: true
+description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with Ultralytics YOLO models. Grasp the nuances of using and converting datasets to this format.
+keywords: Ultralytics, YOLO, oriented bounding boxes, OBB, dataset formats, label formats, DOTA v2, data conversion
+---
+
+# Oriented Bounding Box (OBB) Datasets Overview
+
+Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
+
+## Supported OBB Dataset Formats
+
+### YOLO OBB Format
+
+The YOLO OBB format designates bounding boxes by their four corner points with coordinates normalized between 0 and 1. It follows this format:
+
+```bash
+class_index, x1, y1, x2, y2, x3, y3, x4, y4
+```
+
+Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the bounding box's center point (xy), width, height, and rotation.
+
+<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/259471881-59020fe2-09a4-4dcc-acce-9b0f7cfa40ee.png"></p>
+
+An example of a `*.txt` label file for the above image, which contains an object of class `0` in OBB format, could look like:
+
+```bash
+0 0.780811 0.743961 0.782371 0.74686 0.777691 0.752174 0.776131 0.749758
+```
+
+## Usage
+
+To train a model using these OBB formats:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Create a new YOLOv8n-OBB model from scratch
+        model = YOLO('yolov8n-obb.yaml')
+
+        # Train the model on the DOTAv2 dataset
+        results = model.train(data='DOTAv2.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Train a new YOLOv8n-OBB model on the DOTAv2 dataset
+        yolo detect train data=DOTAv2.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Supported Datasets
+
+Currently, the following datasets with Oriented Bounding Boxes are supported:
+
+- [**DOTA v2**](./dota-v2.md): DOTA (A Large-scale Dataset for Object Detection in Aerial Images) version 2, emphasizes detection from aerial perspectives and contains oriented bounding boxes with 1.7 million instances and 11,268 images.
+
+### Incorporating your own OBB dataset
+
+For those looking to introduce their own datasets with oriented bounding boxes, ensure compatibility with the "YOLO OBB format" mentioned above. Convert your annotations to this required format and detail the paths, classes, and class names in a corresponding YAML configuration file.
+
+## Convert Label Formats
+
+### DOTA Dataset Format to YOLO OBB Format
+
+Transitioning labels from the DOTA dataset format to the YOLO OBB format can be achieved with this script:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics.data.converter import convert_dota_to_yolo_obb
+        
+        convert_dota_to_yolo_obb('path/to/DOTA')
+        ```
+
+This conversion mechanism is instrumental for datasets in the DOTA format, ensuring alignment with the Ultralytics YOLO OBB format.
+
+It's imperative to validate the compatibility of the dataset with your model and adhere to the necessary format conventions. Properly structured datasets are pivotal for training efficient object detection models with oriented bounding boxes.

yolov8-to/docs/datasets/pose/coco.md

@@ -0,0 +1,95 @@
+---
+comments: true
+description: Detailed guide on the special COCO-Pose Dataset in Ultralytics. Learn about its key features, structure, and usage in pose estimation tasks with YOLO.
+keywords: Ultralytics YOLO, COCO-Pose Dataset, Deep Learning, Pose Estimation, Training Models, Dataset YAML, openpose, YOLO
+---
+
+# COCO-Pose Dataset
+
+The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset, designed for pose estimation tasks. It leverages the COCO Keypoints 2017 images and labels to enable the training of models like YOLO for pose estimation tasks.
+
+![Pose sample image](https://user-images.githubusercontent.com/26833433/239691398-d62692dc-713e-4207-9908-2f6710050e5c.jpg)
+
+## Key Features
+
+- COCO-Pose builds upon the COCO Keypoints 2017 dataset which contains 200K images labeled with keypoints for pose estimation tasks.
+- The dataset supports 17 keypoints for human figures, facilitating detailed pose estimation.
+- Like COCO, it provides standardized evaluation metrics, including Object Keypoint Similarity (OKS) for pose estimation tasks, making it suitable for comparing model performance.
+
+## Dataset Structure
+
+The COCO-Pose dataset is split into three subsets:
+
+1. **Train2017**: This subset contains a portion of the 118K images from the COCO dataset, annotated for training pose estimation models.
+2. **Val2017**: This subset has a selection of images used for validation purposes during model training.
+3. **Test2017**: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
+
+## Applications
+
+The COCO-Pose dataset is specifically used for training and evaluating deep learning models in keypoint detection and pose estimation tasks, such as OpenPose. The dataset's large number of annotated images and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners focused on pose estimation.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Pose dataset, the `coco-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml).
+
+!!! example "ultralytics/cfg/datasets/coco-pose.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco-pose.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco-pose.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+The COCO-Pose dataset contains a diverse set of images with human figures annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239690150-a9dc0bd0-7ad9-4b78-a30f-189ed727ea0e.jpg)
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the COCO-Pose dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the COCO-Pose dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

yolov8-to/docs/datasets/pose/coco8-pose.md

@@ -0,0 +1,84 @@
+---
+comments: true
+description: Discover the versatile COCO8-Pose dataset, perfect for testing and debugging pose detection models. Learn how to get started with YOLOv8-pose model training.
+keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, model training, YAML
+---
+
+# COCO8-Pose Dataset
+
+## 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.
+
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
+and [YOLOv8](https://github.com/ultralytics/ultralytics).
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Pose dataset, the `coco8-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
+
+!!! example "ultralytics/cfg/datasets/coco8-pose.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco8-pose.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco8-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+Here are some examples of images from the COCO8-Pose dataset, along with their corresponding annotations:
+
+<img src="https://user-images.githubusercontent.com/26833433/236818283-52eecb96-fc6a-420d-8a26-d488b352dd4c.jpg" alt="Dataset sample image" width="800">
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the COCO8-Pose dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the COCO dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

yolov8-to/docs/datasets/pose/index.md

@@ -0,0 +1,130 @@
+---
+comments: true
+description: Understand the YOLO pose dataset format and learn to use Ultralytics datasets to train your pose estimation models effectively.
+keywords: Ultralytics, YOLO, pose estimation, datasets, training, YAML, keypoints, COCO-Pose, COCO8-Pose, data conversion
+---
+
+# Pose Estimation Datasets Overview
+
+## Supported Dataset Formats
+
+### Ultralytics YOLO format
+
+The dataset label format used for training YOLO pose models is as follows:
+
+1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
+2. One row per object: Each row in the text file corresponds to one object instance in the image.
+3. Object information per row: Each row contains the following information about the object instance:
+    - Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
+    - Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
+    - Object width and height: The width and height of the object, normalized to be between 0 and 1.
+    - Object keypoint coordinates: The keypoints of the object, normalized to be between 0 and 1.
+
+Here is an example of the label format for pose estimation task:
+
+Format with Dim = 2
+
+```
+<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>
+```
+
+Format with Dim = 3
+
+```
+<class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <p2-visibility>
+```
+
+In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of boudning box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
+
+### Dataset YAML format
+
+The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Detection Models. Here is an example of the YAML format used for defining a detection dataset:
+
+```yaml
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8-pose  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+test:  # test images (optional)
+
+# Keypoints
+kpt_shape: [17, 3]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes dictionary
+names:
+  0: person
+```
+
+The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
+
+`names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
+
+(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face.
+For example if we assume five keypoints of facial landmark: [left eye, right eye, nose, left mouth, right mouth], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3] (just exchange the left-right index, i.e 0-1 and 3-4, and do not modify others like nose in this example).
+
+## Usage
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco128-pose.yaml', epochs=100, imgsz=640)
+        ```
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco128-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        ```
+
+## Supported Datasets
+
+This section outlines the datasets that are compatible with Ultralytics YOLO format and can be used for training pose estimation models:
+
+### COCO-Pose
+
+- **Description**: COCO-Pose is a large-scale object detection, segmentation, and pose estimation dataset. It is a subset of the popular COCO dataset and focuses on human pose estimation. COCO-Pose includes multiple keypoints for each human instance.
+- **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses.
+- **Number of Classes**: 1 (Human).
+- **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
+- **Usage**: Suitable for training human pose estimation models.
+- **Additional Notes**: The dataset is rich and diverse, containing over 200k labeled images.
+- [Read more about COCO-Pose](./coco.md)
+
+### COCO8-Pose
+
+- **Description**: [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.
+- **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses.
+- **Number of Classes**: 1 (Human).
+- **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
+- **Usage**: Suitable for testing and debugging object detection models, or for experimenting with new detection approaches.
+- **Additional Notes**: COCO8-Pose is ideal for sanity checks and CI checks.
+- [Read more about COCO8-Pose](./coco8-pose.md)
+
+### Adding your own dataset
+
+If you have your own dataset and would like to use it for training pose estimation models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.
+
+### Conversion Tool
+
+Ultralytics provides a convenient conversion tool to convert labels from the popular COCO dataset format to YOLO format:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics.data.converter import convert_coco
+        
+        convert_coco(labels_dir='path/to/coco/annotations/', use_keypoints=True)
+        ```
+
+This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.

yolov8-to/docs/datasets/segment/coco.md

@@ -0,0 +1,94 @@
+---
+comments: true
+description: Explore the possibilities of the COCO-Seg dataset, designed for object instance segmentation and YOLO model training. Discover key features, dataset structure, applications, and usage.
+keywords: Ultralytics, YOLO, COCO-Seg, dataset, instance segmentation, model training, deep learning, computer vision
+---
+
+# COCO-Seg Dataset
+
+The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO (Common Objects in Context) dataset, is specially designed to aid research in object instance segmentation. It uses the same images as COCO but introduces more detailed segmentation annotations. This dataset is a crucial resource for researchers and developers working on instance segmentation tasks, especially for training YOLO models.
+
+## Key Features
+
+- COCO-Seg retains the original 330K images from COCO.
+- The dataset consists of the same 80 object categories found in the original COCO dataset.
+- Annotations now include more detailed instance segmentation masks for each object in the images.
+- COCO-Seg provides standardized evaluation metrics like mean Average Precision (mAP) for object detection, and mean Average Recall (mAR) for instance segmentation tasks, enabling effective comparison of model performance.
+
+## Dataset Structure
+
+The COCO-Seg dataset is partitioned into three subsets:
+
+1. **Train2017**: This subset contains 118K images for training instance segmentation models.
+2. **Val2017**: This subset includes 5K images used for validation purposes during model training.
+3. **Test2017**: This subset encompasses 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7383) for performance evaluation.
+
+## Applications
+
+COCO-Seg is widely used for training and evaluating deep learning models in instance segmentation, such as the YOLO models. The large number of annotated images, the diversity of object categories, and the standardized evaluation metrics make it an indispensable resource for computer vision researchers and practitioners.
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Seg dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
+
+!!! example "ultralytics/cfg/datasets/coco.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco-seg.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco-seg.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+COCO-Seg, like its predecessor COCO, contains a diverse set of images with various object categories and complex scenes. However, COCO-Seg introduces more detailed instance segmentation masks for each object in the images. Here are some examples of images from the dataset, along with their corresponding instance segmentation masks:
+
+![Dataset sample image](https://user-images.githubusercontent.com/26833433/239690696-93fa8765-47a2-4b34-a6e5-516d0d1c725b.jpg)
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This aids the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the COCO-Seg dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We extend our thanks to the COCO Consortium for creating and maintaining this invaluable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

yolov8-to/docs/datasets/segment/coco8-seg.md

@@ -0,0 +1,84 @@
+---
+comments: true
+description: 'Discover the COCO8-Seg: a compact but versatile instance segmentation dataset ideal for testing Ultralytics YOLOv8 detection approaches. Complete usage guide included.'
+keywords: COCO8-Seg dataset, Ultralytics, YOLOv8, instance segmentation, dataset configuration, YAML, YOLOv8n-seg model, mosaiced dataset images
+---
+
+# COCO8-Seg Dataset
+
+## 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.
+
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
+and [YOLOv8](https://github.com/ultralytics/ultralytics).
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Seg dataset, the `coco8-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml).
+
+!!! example "ultralytics/cfg/datasets/coco8-seg.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco8-seg.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+Here are some examples of images from the COCO8-Seg dataset, along with their corresponding annotations:
+
+<img src="https://user-images.githubusercontent.com/26833433/236818387-f7bde7df-caaa-46d1-8341-1f7504cd11a1.jpg" alt="Dataset sample image" width="800">
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the COCO8-Seg dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the COCO dataset in your research or development work, please cite the following paper:
+
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```
+
+We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).

yolov8-to/docs/datasets/segment/index.md

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+---
+comments: true
+description: Learn how Ultralytics YOLO supports various dataset formats for instance segmentation. This guide includes information on data conversions, auto-annotations, and dataset usage.
+keywords: Ultralytics, YOLO, Instance Segmentation, Dataset, YAML, COCO, Auto-Annotation, Image Segmentation
+---
+
+# Instance Segmentation Datasets Overview
+
+## Supported Dataset Formats
+
+### Ultralytics YOLO format
+
+The dataset label format used for training YOLO segmentation models is as follows:
+
+1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
+2. One row per object: Each row in the text file corresponds to one object instance in the image.
+3. Object information per row: Each row contains the following information about the object instance:
+    - Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
+    - Object bounding coordinates: The bounding coordinates around the mask area, normalized to be between 0 and 1.
+
+The format for a single row in the segmentation dataset file is as follows:
+
+```
+<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>
+```
+
+In this format, `<class-index>` is the index of the class for the object, and `<x1> <y1> <x2> <y2> ... <xn> <yn>` are the bounding coordinates of the object's segmentation mask. The coordinates are separated by spaces.
+
+Here is an example of the YOLO dataset format for a single image with two objects made up of a 3-point segment and a 5-point segment.
+
+```
+0 0.681 0.485 0.670 0.487 0.676 0.487
+1 0.504 0.000 0.501 0.004 0.498 0.004 0.493 0.010 0.492 0.0104
+```
+
+!!! tip "Tip"
+
+      - The length of each row does **not** have to be equal.
+      - Each segmentation label must have a **minimum of 3 xy points**: `<class-index> <x1> <y1> <x2> <y2> <x3> <y3>`
+
+### Dataset YAML format
+
+The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Detection Models. Here is an example of the YAML format used for defining a detection dataset:
+
+```yaml
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8-seg  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+test:  # test images (optional)
+
+# Classes (80 COCO classes)
+names:
+  0: person
+  1: bicycle
+  2: car
+  ...
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+```
+
+The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
+
+`names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
+
+## Usage
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
+        ```
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
+        ```
+
+## Supported Datasets
+
+* [COCO](coco.md): A large-scale dataset designed for object detection, segmentation, and captioning tasks with over 200K labeled images.
+* [COCO8-seg](coco8-seg.md): A smaller dataset for instance segmentation tasks, containing a subset of 8 COCO images with segmentation annotations.
+
+### Adding your own dataset
+
+If you have your own dataset and would like to use it for training segmentation models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.
+
+## Port or Convert Label Formats
+
+### COCO Dataset Format to YOLO Format
+
+You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics.data.converter import convert_coco
+        
+        convert_coco(labels_dir='path/to/coco/annotations/', use_segments=True)
+        ```
+
+This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
+
+Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
+
+## Auto-Annotation
+
+Auto-annotation is an essential feature that allows you to generate a segmentation dataset using a pre-trained detection model. It enables you to quickly and accurately annotate a large number of images without the need for manual labeling, saving time and effort.
+
+### Generate Segmentation Dataset Using a Detection Model
+
+To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics.data.annotator import auto_annotate
+         
+        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
+        ```
+
+Certainly, here is the table updated with code snippets:
+
+| Argument     | Type                    | Description                                                                                                 | Default        |
+|--------------|-------------------------|-------------------------------------------------------------------------------------------------------------|----------------|
+| `data`       | `str`                   | Path to a folder containing images to be annotated.                                                         | `None`         |
+| `det_model`  | `str, optional`         | Pre-trained YOLO detection model. Defaults to `'yolov8x.pt'`.                                               | `'yolov8x.pt'` |
+| `sam_model`  | `str, optional`         | Pre-trained SAM segmentation model. Defaults to `'sam_b.pt'`.                                               | `'sam_b.pt'`   |
+| `device`     | `str, optional`         | Device to run the models on. Defaults to an empty string (CPU or GPU, if available).                        | `''`           |
+| `output_dir` | `str or None, optional` | Directory to save the annotated results. Defaults to a `'labels'` folder in the same directory as `'data'`. | `None`         |
+
+The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
+
+By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.

yolov8-to/docs/datasets/track/index.md

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+---
+comments: true
+description: Understand multi-object tracking datasets, upcoming features and how to use them with YOLO in Python and CLI. Dive in now!.
+keywords: Ultralytics, YOLO, multi-object tracking, datasets, detection, segmentation, pose models, Python, CLI
+---
+
+# Multi-object Tracking Datasets Overview
+
+## Dataset Format (Coming Soon)
+
+Multi-Object Detector doesn't need standalone training and directly supports pre-trained detection, segmentation or Pose models.
+Support for training trackers alone is coming soon
+
+## Usage
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        model = YOLO('yolov8n.pt')
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True)
+        ```
+    === "CLI"
+
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show
+        ```

yolov8-to/docs/guides/hyperparameter-tuning.md

@@ -0,0 +1,96 @@
+---
+comments: true
+description: Dive into hyperparameter tuning in Ultralytics YOLO models. Learn how to optimize performance using the Tuner class and genetic evolution.
+keywords: Ultralytics, YOLO, Hyperparameter Tuning, Tuner Class, Genetic Evolution, Optimization
+---
+
+# Ultralytics YOLO Hyperparameter Tuning Guide
+
+## Introduction
+
+Hyperparameter tuning is not just a one-time set-up but an iterative process aimed at optimizing the machine learning model's performance metrics, such as accuracy, precision, and recall. In the context of Ultralytics YOLO, these hyperparameters could range from learning rate to architectural details, such as the number of layers or types of activation functions used.
+
+### What are Hyperparameters?
+
+Hyperparameters are high-level, structural settings for the algorithm. They are set prior to the training phase and remain constant during it. Here are some commonly tuned hyperparameters in Ultralytics YOLO:
+
+- **Learning Rate**: Determines the step size at each iteration while moving towards a minimum in the loss function.
+- **Batch Size**: Number of training samples utilized in one iteration.
+- **Number of Epochs**: An epoch is one complete forward and backward pass of all the training examples.
+- **Architecture Specifics**: Such as anchor box sizes, number of layers, types of activation functions, etc.
+
+<p align="center">
+  <img width="1000" src="https://user-images.githubusercontent.com/26833433/263858934-4f109a2f-82d9-4d08-8bd6-6fd1ff520bcd.png" alt="Hyperparameter Tuning Visual">
+</p>
+
+For a full list of augmentation hyperparameters used in YOLOv8 please refer to https://docs.ultralytics.com/usage/cfg/#augmentation.
+
+### Genetic Evolution and Mutation
+
+Ultralytics YOLO uses genetic algorithms to optimize hyperparameters. Genetic algorithms are inspired by the mechanism of natural selection and genetics.
+
+- **Mutation**: In the context of Ultralytics YOLO, mutation helps in locally searching the hyperparameter space by applying small, random changes to existing hyperparameters, producing new candidates for evaluation.
+- **Crossover**: Although crossover is a popular genetic algorithm technique, it is not currently used in Ultralytics YOLO for hyperparameter tuning. The focus is mainly on mutation for generating new hyperparameter sets.
+
+## Preparing for Hyperparameter Tuning
+
+Before you begin the tuning process, it's important to:
+
+1. **Identify the Metrics**: Determine the metrics you will use to evaluate the model's performance. This could be AP50, F1-score, or others.
+2. **Set the Tuning Budget**: Define how much computational resources you're willing to allocate. Hyperparameter tuning can be computationally intensive.
+
+## Steps Involved
+
+### Initialize Hyperparameters
+
+Start with a reasonable set of initial hyperparameters. This could either be the default hyperparameters set by Ultralytics YOLO or something based on your domain knowledge or previous experiments.
+
+### Mutate Hyperparameters
+
+Use the `_mutate` method to produce a new set of hyperparameters based on the existing set.
+
+### Train Model
+
+Training is performed using the mutated set of hyperparameters. The training performance is then assessed.
+
+### Evaluate Model
+
+Use metrics like AP50, F1-score, or custom metrics to evaluate the model's performance.
+
+### Log Results
+
+It's crucial to log both the performance metrics and the corresponding hyperparameters for future reference.
+
+### Repeat
+
+The process is repeated until either the set number of iterations is reached or the performance metric is satisfactory.
+
+## Usage Example
+
+Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning:
+
+!!! example ""
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+        
+        # Initialize the YOLO model
+        model = YOLO('yolov8n.pt')
+        
+        # Perform hyperparameter tuning
+        model.tune(data='coco8.yaml', imgsz=640, epochs=30, iterations=300)
+        ```
+
+## Conclusion
+
+The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful, thanks to its genetic algorithm-based approach focused on mutation. Following the steps outlined in this guide will assist you in systematically tuning your model to achieve better performance.
+
+### Further Reading
+
+1. [Hyperparameter Optimization in Wikipedia](https://en.wikipedia.org/wiki/Hyperparameter_optimization)
+2. [YOLOv5 Hyperparameter Evolution Guide](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
+3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](https://docs.ultralytics.com/integrations/ray-tune/)
+
+For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to our support team.