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ultralytics 8.0.193 add Raspberry Pi guide to Docs (#5230)

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docs/datasets/pose/tiger-pose.md
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@ -7,7 +7,8 @@ keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, mode
# Tiger-Pose Dataset
## Introduction
[Ultralytics](https://ultralytics.com) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 250 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithm.
[Ultralytics](https://ultralytics.com) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithm.
Despite its manageable size of 210 images, tiger-pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for pose estimation.
@ -18,7 +19,6 @@ and [YOLOv8](https://github.com/ultralytics/ultralytics).
A YAML (Yet Another Markup Language) file serves as the means to specify the configuration details of a dataset. It encompasses crucial data such as file paths, class definitions, and other pertinent information. Specifically, for the `tiger-pose.yaml` file, you can check [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
!!! example "ultralytics/cfg/datasets/tiger-pose.yaml"
```yaml
@ -54,7 +54,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
Here are some examples of images from the Tiger-Pose dataset, along with their corresponding annotations:
<img src="https://user-images.githubusercontent.com/62513924/272491921-c963d2bf-505f-4a15-abd7-259de302cffa.jpg" alt="Dataset sample image" width="800">
<img src="https://user-images.githubusercontent.com/62513924/272491921-c963d2bf-505f-4a15-abd7-259de302cffa.jpg" alt="Dataset sample image" width="100%">
- **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.
@ -72,17 +72,17 @@ The example showcases the variety and complexity of the images in the Tiger-Pose
# Load a model
model = YOLO('path/to/best.pt') # load a tiger-pose trained model
# Run Inference
# Run inference
results = model.predict(source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
# Run inference using a tiger-pose trained model
yolo task=pose mode=predict source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True model="path/to/best.pt"
```
## Citations and Acknowledgments
The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).