ultralytics 8.0.211 README language links (#6370)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
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@ -77,7 +77,7 @@ Train a detection model for 10 epochs with an initial learning_rate of 0.01:
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yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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```
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You can find more [instructions to use the Ultralytics CLI here](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli).
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You can find more [instructions to use the Ultralytics CLI here](../quickstart.md#use-ultralytics-with-cli).
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## Quickstart from a Notebook
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@ -114,7 +114,7 @@ pip install onnx>=1.12.0
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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.
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Run some predictions using the [Ultralytics CLI](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-cli):
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Run some predictions using the [Ultralytics CLI](../quickstart.md#use-ultralytics-with-cli):
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```bash
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%%bash
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@ -122,7 +122,7 @@ source activate yolov8env
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yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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Or with the [Ultralytics Python interface](https://docs.ultralytics.com/quickstart/#use-ultralytics-with-python), for example to train the model:
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Or with the [Ultralytics Python interface](../quickstart.md#use-ultralytics-with-python), for example to train the model:
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```python
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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
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---
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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.
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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
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---
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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/).
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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
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<img width="640" src="https://user-images.githubusercontent.com/26833433/263858934-4f109a2f-82d9-4d08-8bd6-6fd1ff520bcd.png" alt="Hyperparameter Tuning Visual">
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</p>
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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).
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For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation).
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### Genetic Evolution and Mutation
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@ -200,7 +200,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
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### Further Reading
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1. [Hyperparameter Optimization in Wikipedia](https://en.wikipedia.org/wiki/Hyperparameter_optimization)
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2. [YOLOv5 Hyperparameter Evolution Guide](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
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3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](https://docs.ultralytics.com/integrations/ray-tune/)
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2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md)
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3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md)
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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
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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.
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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!
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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!
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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!
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## Setup
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- Your annotations should be in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
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- Your annotations should be in the [YOLO detection format](../datasets/detect/index.md).
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- This guide assumes that annotation files are locally available.
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@ -52,7 +52,7 @@ Without further ado, let's dive in!
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- The Ultralytics library: `pip install -U ultralytics`. Alternatively, you can clone the official [repo](https://github.com/ultralytics/ultralytics).
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- Scikit-learn, pandas, and PyYAML: `pip install -U scikit-learn pandas pyyaml`.
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2. Verify that your annotations are in the [YOLO detection format](https://docs.ultralytics.com/datasets/detect/).
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2. Verify that your annotations are in the [YOLO detection format](../datasets/detect/index.md).
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- 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
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## How to Select the Right Deployment Option for Your YOLOv8 Model
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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.
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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.
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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.
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### YOLOv8’s Deployment Options
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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/).
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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).
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#### PyTorch
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@ -94,7 +94,7 @@ OpenVINO is an Intel toolkit designed to facilitate the deployment of deep learn
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- **Hardware Acceleration**: Tailored for acceleration on Intel hardware, leveraging dedicated instruction sets and hardware features.
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For more details on deployment using OpenVINO, refer to the Ultralytics Integration documentation: [Intel OpenVINO Export](https://docs.ultralytics.com/integrations/openvino/).
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For more details on deployment using OpenVINO, refer to the Ultralytics Integration documentation: [Intel OpenVINO Export](../integrations/openvino.md).
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#### TensorRT
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@ -260,7 +260,7 @@ ncnn is a high-performance neural network inference framework optimized for the
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## Comparative Analysis of YOLOv8 Deployment Options
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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).
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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).
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| Deployment Option | Performance Benchmarks | Compatibility and Integration | Community Support and Ecosystem | Case Studies | Maintenance and Updates | Security Considerations | Hardware Acceleration |
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|-------------------|-------------------------------------------------|------------------------------------------------|-----------------------------------------------|--------------------------------------------|---------------------------------------------|---------------------------------------------------|------------------------------------|
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@ -292,7 +292,7 @@ When you're getting started with YOLOv8, having a helpful community and support
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### Official Documentation and Resources
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- **Ultralytics YOLOv8 Docs:** The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
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- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
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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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- Consider using virtual environments to avoid conflicts.
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- Follow the [official installation guide](https://docs.ultralytics.com/quickstart/) step by step.
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- Follow the [official installation guide](../quickstart.md) step by step.
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Additionally, here are some common installation issues users have encountered, along with their respective solutions:
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### Official Documentation and Resources
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**Ultralytics YOLOv8 Docs**: The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
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**Ultralytics YOLOv8 Docs**: The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
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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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## How to Calculate Metrics for YOLOv8 Model
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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.
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Now, we can explore [YOLOv8's Validation mode](../modes/val.md) that can be used to compute the above discussed evaluation metrics.
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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?
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### Official Documentation and Resources:
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- **Ultralytics YOLOv8 Docs:** The [official documentation](https://docs.ultralytics.com/) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
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- **Ultralytics YOLOv8 Docs:** The [official documentation](../index.md) provides a comprehensive overview of YOLOv8, along with guides on installation, usage, and troubleshooting.
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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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## Usage
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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.
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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.
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!!! example "Usage"
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)
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```
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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.
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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.
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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.
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@ -170,4 +170,4 @@ Explore [Comet ML's official documentation](https://www.comet.com/docs/v2/integr
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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.
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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.
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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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## Training Integrations
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- [Comet ML](https://www.comet.ml/): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
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- [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
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- [ClearML](https://clear.ml/): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.
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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.
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For more detailed information and instructions on using OpenVINO, refer to the [official OpenVINO documentation](https://docs.openvinotoolkit.org/latest/index.html).
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For more detailed information and instructions on using OpenVINO, refer to the [official OpenVINO documentation](https://docs.openvino.ai/).
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## Real-world Applications
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| Manufacturing | Sports | Safety |
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|:-----------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|
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|  |  |  |
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| Vehicle Spare Parts Detection | Football Player Detection | People Fall Detection |
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| Manufacturing | Sports | Safety |
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|:-------------------------------------------------:|:----------------------------------------------------:|:-------------------------------------------:|
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| ![Vehicle Spare Parts Detection][car spare parts] | ![Football Player Detection][football player detect] | ![People Fall Detection][human fall detect] |
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| Vehicle Spare Parts Detection | Football Player Detection | People Fall Detection |
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## Why Use Ultralytics YOLO for Inference?
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| `xyxyn` | Property (`torch.Tensor`) | Return the boxes in xyxy format normalized by original image size. |
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| `xywhn` | Property (`torch.Tensor`) | Return the boxes in xywh format normalized by original image size. |
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For more details see the `Boxes` class [documentation](../reference/engine/results.md).
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For more details see the `Boxes` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Boxes).
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### Masks
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| `xyn` | Property (`torch.Tensor`) | A list of normalized segments represented as tensors. |
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| `xy` | Property (`torch.Tensor`) | A list of segments in pixel coordinates represented as tensors. |
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For more details see the `Masks` class [documentation](../reference/engine/results.md).
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For more details see the `Masks` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Masks).
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### Keypoints
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| `xy` | Property (`torch.Tensor`) | A list of keypoints in pixel coordinates represented as tensors. |
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| `conf` | Property (`torch.Tensor`) | Returns confidence values of keypoints if available, else None. |
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For more details see the `Keypoints` class [documentation](../reference/engine/results.md).
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For more details see the `Keypoints` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Keypoints).
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### Probs
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| `top1conf` | Property (`torch.Tensor`) | Confidence of the top 1 class. |
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| `top5conf` | Property (`torch.Tensor`) | Confidences of the top 5 classes. |
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For more details see the `Probs` class [documentation](../reference/engine/results.md).
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For more details see the `Probs` class [documentation](../reference/engine/results.md#ultralytics.engine.results.Probs).
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## Plotting Results
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```
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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'.
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[car spare parts]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1
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[football player detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442
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[human fall detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43
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## Real-world Applications
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| Transportation | Retail | Aquaculture |
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|:----------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
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|  |  |  |
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| Vehicle Tracking | People Tracking | Fish Tracking |
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| Transportation | Retail | Aquaculture |
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|:----------------------------------:|:--------------------------------:|:----------------------------:|
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| ![Vehicle Tracking][vehicle track] | ![People Tracking][people track] | ![Fish Tracking][fish track] |
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| Vehicle Tracking | People Tracking | Fish Tracking |
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## Features at a Glance
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### Tracking Arguments
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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.
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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.
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!!! example ""
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@ -352,3 +352,7 @@ By contributing to this section, you help expand the scope of tracking solutions
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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!
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Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏!
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|
||||
[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
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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/
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue