ultralytics 8.0.193 add Raspberry Pi guide to Docs (#5230)
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: DaanKwF <108017202+DaanKwF@users.noreply.github.com>
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Glenn Jocher
GitHub
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@ -60,8 +60,7 @@ The `train` and `val` fields specify the paths to the directories containing the
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`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.
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(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face.
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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).
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(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).
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## Usage
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@ -109,6 +108,15 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for
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- **Additional Notes**: COCO8-Pose is ideal for sanity checks and CI checks.
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- [Read more about COCO8-Pose](./coco8-pose.md)
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### Tiger-Pose
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- **Description**: [Ultralytics](https://ultralytics.com) This animal pose 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.
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- **Label Format**: Same as Ultralytics YOLO format as described above, with 12 keypoints for animal pose and no visible dimension.
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- **Number of Classes**: 1 (Tiger).
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- **Keypoints**: 12 keypoints.
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- **Usage**: Great for animal pose or any other pose that is not human-based.
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- [Read more about Tiger-Pose](./tiger-pose.md)
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### Adding your own dataset
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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.
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@ -7,7 +7,8 @@ keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, mode
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# Tiger-Pose Dataset
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## Introduction
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[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.
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[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.
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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.
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@ -18,7 +19,6 @@ and [YOLOv8](https://github.com/ultralytics/ultralytics).
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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).
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!!! example "ultralytics/cfg/datasets/tiger-pose.yaml"
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```yaml
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@ -54,7 +54,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
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Here are some examples of images from the Tiger-Pose dataset, along with their corresponding annotations:
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<img src="https://user-images.githubusercontent.com/62513924/272491921-c963d2bf-505f-4a15-abd7-259de302cffa.jpg" alt="Dataset sample image" width="800">
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<img src="https://user-images.githubusercontent.com/62513924/272491921-c963d2bf-505f-4a15-abd7-259de302cffa.jpg" alt="Dataset sample image" width="100%">
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- **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.
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@ -72,17 +72,17 @@ The example showcases the variety and complexity of the images in the Tiger-Pose
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# Load a model
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model = YOLO('path/to/best.pt') # load a tiger-pose trained model
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# Run Inference
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# Run inference
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results = model.predict(source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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# Run inference using a tiger-pose trained model
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yolo task=pose mode=predict source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True model="path/to/best.pt"
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```
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## Citations and Acknowledgments
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The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
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