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99 lines
4.5 KiB
99 lines
4.5 KiB
--- |
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comments: true |
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description: Discover the versatile Tiger-Pose dataset, perfect for testing and debugging pose detection models. Learn how to get started with YOLOv8-pose model training. |
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keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, model training, YAML |
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--- |
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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 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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This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com) |
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and [YOLOv8](https://github.com/ultralytics/ultralytics). |
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<p align="center"> |
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<br> |
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<iframe width="720" height="405" src="https://www.youtube.com/embed/Gc6K5eKrTNQ" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB |
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</p> |
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## Dataset YAML |
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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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--8<-- "ultralytics/cfg/datasets/tiger-pose.yaml" |
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``` |
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## Usage |
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To train a YOLOv8n-pose model on the Tiger-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. |
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!!! Example "Train Example" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO('yolov8n-pose.pt') # load a pretrained model (recommended for training) |
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# Train the model |
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results = model.train(data='tiger-pose.yaml', epochs=100, imgsz=640) |
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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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yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n.pt epochs=100 imgsz=640 |
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``` |
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## Sample Images and Annotations |
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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="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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The example showcases the variety and complexity of the images in the Tiger-Pose dataset and the benefits of using mosaicing during the training process. |
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## Inference Example |
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!!! Example "Inference Example" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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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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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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# 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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