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18
README.md
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@ -4,7 +4,7 @@
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
</p>
[English](README.md) | [简体中文](README.zh-CN.md)
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/)
<br>
<div>
@ -44,7 +44,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
</div>
</div>
## <div align="center">Documentation</div>
## Documentation
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.
@ -94,11 +94,11 @@ 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.
See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
</details>
## <div align="center">Models</div>
## Models
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.
@ -203,7 +203,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
</details>
## <div align="center">Integrations</div>
## Integrations
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.
@ -231,14 +231,14 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| 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>
## Ultralytics HUB
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" alt="Ultralytics HUB preview image"></a>
## <div align="center">Contribute</div>
## Contribute
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!
@ -247,14 +247,14 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou
<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>
## License
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>
## Contact
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!

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@ -4,7 +4,7 @@
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
</p>
[English](README.md) | [简体中文](README.zh-CN.md)
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/)
<br>
<div>
@ -44,7 +44,7 @@
</div>
</div>
## <div align="center">文档</div>
## 文档
请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关训练、验证、预测和部署的完整文档。
@ -94,11 +94,11 @@ results = model("https://ultralytics.com/images/bus.jpg") # 对图像进行预
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。
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>
## 模型
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。
@ -202,7 +202,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
</details>
## <div align="center">集成</div>
## 集成
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。
@ -230,14 +230,14 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
| :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
| 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！）自动跟踪、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>
## Ultralytics HUB
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" alt="Ultralytics HUB preview image"></a>
## <div align="center">贡献</div>
## 贡献
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！🙏
@ -246,14 +246,14 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
<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>
## 许可证
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>
## 联系方式
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！

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@ -77,7 +77,7 @@ Train a detection model for 10 epochs with an initial learning_rate of 0.01:
yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
```
You can find more [instructions to use the Ultralytics CLI here](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli).
You can find more [instructions to use the Ultralytics CLI here](../quickstart.md#use-ultralytics-with-cli).
## Quickstart from a Notebook
@ -114,7 +114,7 @@ pip install onnx>=1.12.0
Note that we need to use the `source activate yolov8env` for all the %%bash cells, to make sure that the %%bash cell uses environment we want.
Run some predictions using the [Ultralytics CLI](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli):
Run some predictions using the [Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli):
```bash
%%bash
@ -122,7 +122,7 @@ source activate yolov8env
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
```
Or with the [Ultralytics Python interface](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-python), for example to train the model:
Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with-python), for example to train the model:
```python
from ultralytics import YOLO

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@ -129,4 +129,4 @@ And that's it! Your Conda installation will now use `libmamba` as the solver, wh
---
Congratulations! You have successfully set up a Conda environment, installed the Ultralytics package, and are now ready to explore its rich functionalities. Feel free to dive deeper into the [Ultralytics documentation](https://docs.ultralytics.com/) for more advanced tutorials and examples.
Congratulations! You have successfully set up a Conda environment, installed the Ultralytics package, and are now ready to explore its rich functionalities. Feel free to dive deeper into the [Ultralytics documentation](../index.md) for more advanced tutorials and examples.

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@ -116,4 +116,4 @@ Replace `/path/on/host` with the directory path on your local machine and `/path
---
Congratulations! You're now set up to use Ultralytics with Docker and ready to take advantage of its powerful capabilities. For alternate installation methods, feel free to explore the [Ultralytics quickstart documentation](https://docs.ultralytics.com/quickstart/).
Congratulations! You're now set up to use Ultralytics with Docker and ready to take advantage of its powerful capabilities. For alternate installation methods, feel free to explore the [Ultralytics quickstart documentation](../quickstart.md).

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@ -23,7 +23,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are
<img width="640" 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](https://docs.ultralytics.com/usage/cfg/#augmentation).
For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation).
### Genetic Evolution and Mutation
@ -200,7 +200,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
### 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/)
2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md)
3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md)
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 us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord).

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@ -31,6 +31,6 @@ Here's a compilation of in-depth guides to help you master different aspects of
We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our guides, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.
To get started, please read our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!
To get started, please read our [Contributing Guide](../help/contributing.md) for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!
Let's work together to make the Ultralytics YOLO ecosystem more robust and versatile 🙏!

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@ -20,7 +20,7 @@ Without further ado, let's dive in!
## Setup
- Your annotations should be in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
- Your annotations should be in the [YOLO detection format](../datasets/detect/index.md).
- This guide assumes that annotation files are locally available.
@ -52,7 +52,7 @@ Without further ado, let's dive in!
- The Ultralytics library: `pip install -U ultralytics`. Alternatively, you can clone the official [repo](https://github.com/ultralytics/ultralytics).
- Scikit-learn, pandas, and PyYAML: `pip install -U scikit-learn pandas pyyaml`.
2. Verify that your annotations are in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
2. Verify that your annotations are in the [YOLO detection format](../datasets/detect/index.md).
- For this tutorial, all annotation files are found in the `Fruit-Detection/labels` directory.

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@ -14,13 +14,13 @@ This guide walks you through YOLOv8’s deployment options and the essential fac
## How to Select the Right Deployment Option for Your YOLOv8 Model
When it's time to deploy your YOLOv8 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLOv8 Modes documentation](https://docs.ultralytics.com/modes/export/#usage-examples), the model.export() function allows for converting your trained model into a variety of formats tailored to diverse environments and performance requirements.
When it's time to deploy your YOLOv8 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLOv8 Modes documentation](../modes/export.md#usage-examples), the model.export() function allows for converting your trained model into a variety of formats tailored to diverse environments and performance requirements.
The ideal format depends on your model's intended operational context, balancing speed, hardware constraints, and ease of integration. In the following section, we'll take a closer look at each export option, understanding when to choose each one.
### YOLOv8’s Deployment Options
Let’s walk through the different YOLOv8 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](https://docs.ultralytics.com/modes/export/).
Let’s walk through the different YOLOv8 deployment options. For a detailed walkthrough of the export process, visit the [Ultralytics documentation page on exporting](../modes/export.md).
#### PyTorch
@ -94,7 +94,7 @@ OpenVINO is an Intel toolkit designed to facilitate the deployment of deep learn
- **Hardware Acceleration**: Tailored for acceleration on Intel hardware, leveraging dedicated instruction sets and hardware features.
For more details on deployment using OpenVINO, refer to the Ultralytics Integration documentation: [Intel OpenVINO Export](https://docs.ultralytics.com/integrations/openvino/).
For more details on deployment using OpenVINO, refer to the Ultralytics Integration documentation: [Intel OpenVINO Export](../integrations/openvino.md).
#### TensorRT
@ -260,7 +260,7 @@ ncnn is a high-performance neural network inference framework optimized for the
## Comparative Analysis of YOLOv8 Deployment Options
The following table provides a snapshot of the various deployment options available for YOLOv8 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](https://docs.ultralytics.com/modes/export/#export-formats).
The following table provides a snapshot of the various deployment options available for YOLOv8 models, helping you to assess which may best fit your project needs based on several critical criteria. For an in-depth look at each deployment option's format, please see the [Ultralytics documentation page on export formats](../modes/export.md#export-formats).
| Deployment Option | Performance Benchmarks | Compatibility and Integration | Community Support and Ecosystem | Case Studies | Maintenance and Updates | Security Considerations | Hardware Acceleration |
|-------------------|-------------------------------------------------|------------------------------------------------|-----------------------------------------------|--------------------------------------------|---------------------------------------------|---------------------------------------------------|------------------------------------|
@ -292,7 +292,7 @@ When you're getting started with YOLOv8, having a helpful community and support
### Official Documentation and Resources
- **Ultralytics YOLOv8 Docs:** The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
These resources will help you tackle challenges and stay updated on the latest trends and best practices in the YOLOv8 community.

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@ -26,7 +26,7 @@ Installation errors can arise due to various reasons, such as incompatible versi
- Consider using virtual environments to avoid conflicts.
- Follow the [official installation guide](https://docs.ultralytics.com/quickstart/) step by step.
- Follow the [official installation guide](../quickstart.md) step by step.
Additionally, here are some common installation issues users have encountered, along with their respective solutions:
@ -263,7 +263,7 @@ Engaging with a community of like-minded individuals can significantly enhance y
### Official Documentation and Resources
**Ultralytics YOLOv8 Docs**: The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
**Ultralytics YOLOv8 Docs**: The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
These resources should provide a solid foundation for troubleshooting and improving your YOLOv8 projects, as well as connecting with others in the YOLOv8 community.

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@ -26,7 +26,7 @@ Let’s start by discussing some metrics that are not only important to YOLOv8 b
## How to Calculate Metrics for YOLOv8 Model
Now, we can explore [YOLOv8's Validation mode](https://docs.ultralytics.com/modes/val/) that can be used to compute the above discussed evaluation metrics.
Now, we can explore [YOLOv8's Validation mode](../modes/val.md) that can be used to compute the above discussed evaluation metrics.
Using the validation mode is simple. Once you have a trained model, you can invoke the model.val() function. This function will then process the validation dataset and return a variety of performance metrics. But what do these metrics mean? And how should you interpret them?
@ -152,7 +152,7 @@ Tapping into a community of enthusiasts and experts can amplify your journey wit
### Official Documentation and Resources:
- **Ultralytics YOLOv8 Docs:** The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
Using these resources will not only guide you through any challenges but also keep you updated with the latest trends and best practices in the YOLOv8 community.

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@ -59,7 +59,7 @@ comet_ml.init(project_name="comet-example-yolov8-coco128")
## Usage
Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](https://docs.ultralytics.com/models/). This will help you choose the most appropriate model for your project requirements.
Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -82,7 +82,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
)
```
After running the training code, Comet ML will create an experiment in your Comet workspace to track the run automatically. You will then be provided with a link to view the detailed logging of your [YOLOv8 model's training](https://docs.ultralytics.com/modes/train/) process.
After running the training code, Comet ML will create an experiment in your Comet workspace to track the run automatically. You will then be provided with a link to view the detailed logging of your [YOLOv8 model's training](../modes/train.md) process.
Comet automatically logs the following data with no additional configuration: metrics such as mAP and loss, hyperparameters, model checkpoints, interactive confusion matrix, and image bounding box predictions.
@ -170,4 +170,4 @@ Explore [Comet ML's official documentation](https://www.comet.com/docs/v2/integr
Furthermore, if you're looking to dive deeper into the practical applications of YOLOv8, specifically for image segmentation tasks, this detailed guide on [fine-tuning YOLOv8 with Comet ML](https://www.comet.com/site/blog/fine-tuning-yolov8-for-image-segmentation-with-comet/) offers valuable insights and step-by-step instructions to enhance your model's performance.
Additionally, to explore other exciting integrations with Ultralytics, check out the [integration guide page](https://docs.ultralytics.com/integrations/), which offers a wealth of resources and information.
Additionally, to explore other exciting integrations with Ultralytics, check out the [integration guide page](../integrations/index.md), which offers a wealth of resources and information.

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@ -16,7 +16,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
## Training Integrations
- [Comet ML](https://www.comet.ml/): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
- [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
- [ClearML](https://clear.ml/): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.

2
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@ -281,4 +281,4 @@ For the Intel® Data Center GPU Flex Series, the OpenVINO format was able to del
The benchmarks underline the effectiveness of OpenVINO as a tool for deploying deep learning models. By converting models to the OpenVINO format, developers can achieve significant performance improvements, making it easier to deploy these models in real-world applications.
For more detailed information and instructions on using OpenVINO, refer to the [official OpenVINO documentation](https://docs.openvinotoolkit.org/latest/index.html).
For more detailed information and instructions on using OpenVINO, refer to the [official OpenVINO documentation](https://docs.openvino.ai/).

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@ -26,8 +26,8 @@ In the world of machine learning and computer vision, the process of making sens
## Real-world Applications
| Manufacturing | Sports | Safety |
|:-----------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|
| ![Vehicle Spare Parts Detection](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1) | ![Football Player Detection](https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442) | ![People Fall Detection](https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43) |
|:-------------------------------------------------:|:----------------------------------------------------:|:-------------------------------------------:|
| ![Vehicle Spare Parts Detection][car spare parts] | ![Football Player Detection][football player detect] | ![People Fall Detection][human fall detect] |
| Vehicle Spare Parts Detection | Football Player Detection | People Fall Detection |
## Why Use Ultralytics YOLO for Inference?
@ -495,7 +495,7 @@ Here is a table for the `Boxes` class methods and properties, including their na
| `xyxyn` | Property (`torch.Tensor`) | Return the boxes in xyxy format normalized by original image size. |
| `xywhn` | Property (`torch.Tensor`) | Return the boxes in xywh format normalized by original image size. |
For more details see the `Boxes` class [documentation](../reference/engine/results.md).
For more details see the `Boxes` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Boxes).
### Masks
@ -528,7 +528,7 @@ Here is a table for the `Masks` class methods and properties, including their na
| `xyn` | Property (`torch.Tensor`) | A list of normalized segments represented as tensors. |
| `xy` | Property (`torch.Tensor`) | A list of segments in pixel coordinates represented as tensors. |
For more details see the `Masks` class [documentation](../reference/engine/results.md).
For more details see the `Masks` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Masks).
### Keypoints
@ -562,7 +562,7 @@ Here is a table for the `Keypoints` class methods and properties, including thei
| `xy` | Property (`torch.Tensor`) | A list of keypoints in pixel coordinates represented as tensors. |
| `conf` | Property (`torch.Tensor`) | Returns confidence values of keypoints if available, else None. |
For more details see the `Keypoints` class [documentation](../reference/engine/results.md).
For more details see the `Keypoints` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Keypoints).
### Probs
@ -597,7 +597,7 @@ Here's a table summarizing the methods and properties for the `Probs` class:
| `top1conf` | Property (`torch.Tensor`) | Confidence of the top 1 class. |
| `top5conf` | Property (`torch.Tensor`) | Confidences of the top 5 classes. |
For more details see the `Probs` class [documentation](../reference/engine/results.md).
For more details see the `Probs` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Probs).
## Plotting Results
@ -713,3 +713,7 @@ Here's a Python script using OpenCV (`cv2`) and YOLOv8 to run inference on video
```
This script will run predictions on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.
[car spare parts]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1
[football player detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442
[human fall detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43

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@ -33,8 +33,8 @@ The output from Ultralytics trackers is consistent with standard object detectio
## Real-world Applications
| Transportation | Retail | Aquaculture |
|:----------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| ![Vehicle Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab) | ![People Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527) | ![Fish Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142) |
|:----------------------------------:|:--------------------------------:|:----------------------------:|
| ![Vehicle Tracking][vehicle track] | ![People Tracking][people track] | ![Fish Tracking][fish track] |
| Vehicle Tracking | People Tracking | Fish Tracking |
## Features at a Glance
@ -95,7 +95,7 @@ As can be seen in the above usage, tracking is available for all Detect, Segment
### Tracking Arguments
Tracking configuration shares properties with Predict mode, such as `conf`, `iou`, and `show`. For further configurations, refer to the [Predict](https://docs.ultralytics.com/modes/predict/) model page.
Tracking configuration shares properties with Predict mode, such as `conf`, `iou`, and `show`. For further configurations, refer to the [Predict](../modes/predict.md#inference-arguments) model page.
!!! example ""
@ -352,3 +352,7 @@ By contributing to this section, you help expand the scope of tracking solutions
To initiate your contribution, please refer to our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for comprehensive instructions on submitting a Pull Request (PR) 🛠️. We are excited to see what you bring to the table!
Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏!
[vehicle track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab
[people track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527
[fish track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142

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@ -232,7 +232,7 @@ To use a logger, select it from the dropdown menu in the code snippet above and
### Comet
[Comet](https://www.comet.ml/site/) is a platform that allows data scientists and developers to track, compare, explain and optimize experiments and models. It provides functionalities such as real-time metrics, code diffs, and hyperparameters tracking.
[Comet](../integrations/comet.md) is a platform that allows data scientists and developers to track, compare, explain and optimize experiments and models. It provides functionalities such as real-time metrics, code diffs, and hyperparameters tracking.
To use Comet:

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@ -258,6 +258,7 @@ nav:
- Triton Inference Server: guides/triton-inference-server.md
- Integrations:
- integrations/index.md
- Comet ML: integrations/comet.md
- OpenVINO: integrations/openvino.md
- Ray Tune: integrations/ray-tune.md
- Roboflow: integrations/roboflow.md

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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Script to fix broken Markdown links and front matter in language-specific directories.
Script to fix broken Markdown links and front matter in language-specific directories zh, ko, ja, ru, de, fr, es, pt.
This script processes markdown files in language-specific directories (like /zh/). It finds Markdown links and checks
their existence. If a link is broken and does not exist in the language-specific directory but exists in the /en/

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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.210'
__version__ = '8.0.211'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM