Add Chinese Modes and Tasks Docs (#6274)
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>
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95
docs/en/datasets/pose/coco.md
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docs/en/datasets/pose/coco.md
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---
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comments: true
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description: Detailed guide on the special COCO-Pose Dataset in Ultralytics. Learn about its key features, structure, and usage in pose estimation tasks with YOLO.
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keywords: Ultralytics YOLO, COCO-Pose Dataset, Deep Learning, Pose Estimation, Training Models, Dataset YAML, openpose, YOLO
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---
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# COCO-Pose Dataset
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The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset, designed for pose estimation tasks. It leverages the COCO Keypoints 2017 images and labels to enable the training of models like YOLO for pose estimation tasks.
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## Key Features
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- COCO-Pose builds upon the COCO Keypoints 2017 dataset which contains 200K images labeled with keypoints for pose estimation tasks.
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- The dataset supports 17 keypoints for human figures, facilitating detailed pose estimation.
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- Like COCO, it provides standardized evaluation metrics, including Object Keypoint Similarity (OKS) for pose estimation tasks, making it suitable for comparing model performance.
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## Dataset Structure
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The COCO-Pose dataset is split into three subsets:
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1. **Train2017**: This subset contains a portion of the 118K images from the COCO dataset, annotated for training pose estimation models.
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2. **Val2017**: This subset has a selection of images used for validation purposes during model training.
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3. **Test2017**: This subset consists of images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
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## Applications
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The COCO-Pose dataset is specifically used for training and evaluating deep learning models in keypoint detection and pose estimation tasks, such as OpenPose. The dataset's large number of annotated images and standardized evaluation metrics make it an essential resource for computer vision researchers and practitioners focused on pose estimation.
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## Dataset YAML
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Pose dataset, the `coco-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml).
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!!! example "ultralytics/cfg/datasets/coco-pose.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/coco-pose.yaml"
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```
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## Usage
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To train a YOLOv8n-pose model on the COCO-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='coco-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 detect train data=coco-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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The COCO-Pose dataset contains a diverse set of images with human figures annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:
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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 COCO-Pose dataset and the benefits of using mosaicing during the training process.
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## Citations and Acknowledgments
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If you use the COCO-Pose dataset in your research or development work, please cite the following paper:
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!!! note ""
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=== "BibTeX"
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```bibtex
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@misc{lin2015microsoft,
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title={Microsoft COCO: Common Objects in Context},
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author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
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year={2015},
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eprint={1405.0312},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
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docs/en/datasets/pose/coco8-pose.md
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docs/en/datasets/pose/coco8-pose.md
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---
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comments: true
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description: Discover the versatile COCO8-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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# COCO8-Pose Dataset
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## Introduction
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[Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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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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## Dataset YAML
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Pose dataset, the `coco8-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
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!!! example "ultralytics/cfg/datasets/coco8-pose.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/coco8-pose.yaml"
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```
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## Usage
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To train a YOLOv8n-pose model on the COCO8-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='coco8-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 detect train data=coco8-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 COCO8-Pose dataset, along with their corresponding annotations:
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<img src="https://user-images.githubusercontent.com/26833433/236818283-52eecb96-fc6a-420d-8a26-d488b352dd4c.jpg" alt="Dataset sample image" width="800">
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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 COCO8-Pose dataset and the benefits of using mosaicing during the training process.
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## Citations and Acknowledgments
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If you use the COCO dataset in your research or development work, please cite the following paper:
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!!! note ""
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=== "BibTeX"
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```bibtex
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@misc{lin2015microsoft,
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title={Microsoft COCO: Common Objects in Context},
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author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
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year={2015},
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eprint={1405.0312},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
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docs/en/datasets/pose/index.md
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---
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comments: true
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description: Understand the YOLO pose dataset format and learn to use Ultralytics datasets to train your pose estimation models effectively.
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keywords: Ultralytics, YOLO, pose estimation, datasets, training, YAML, keypoints, COCO-Pose, COCO8-Pose, data conversion
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---
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# Pose Estimation Datasets Overview
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## Supported Dataset Formats
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### Ultralytics YOLO format
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The dataset label format used for training YOLO pose models is as follows:
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1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
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2. One row per object: Each row in the text file corresponds to one object instance in the image.
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3. Object information per row: Each row contains the following information about the object instance:
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- Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
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- Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
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- Object width and height: The width and height of the object, normalized to be between 0 and 1.
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- Object keypoint coordinates: The keypoints of the object, normalized to be between 0 and 1.
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Here is an example of the label format for pose estimation task:
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Format with Dim = 2
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```
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<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>
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```
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Format with Dim = 3
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```
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<class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <p2-visibility>
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```
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In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of bounding box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
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### Dataset YAML format
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The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Detection Models. Here is an example of the YAML format used for defining a detection dataset:
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```yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/coco8-pose # dataset root dir
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train: images/train # train images (relative to 'path') 4 images
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val: images/val # val images (relative to 'path') 4 images
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test: # test images (optional)
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# Keypoints
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kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
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flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
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# Classes dictionary
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names:
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0: person
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```
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The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
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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. 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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!!! 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='coco128-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 detect train data=coco128-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
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```
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## Supported Datasets
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This section outlines the datasets that are compatible with Ultralytics YOLO format and can be used for training pose estimation models:
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### COCO-Pose
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- **Description**: COCO-Pose is a large-scale object detection, segmentation, and pose estimation dataset. It is a subset of the popular COCO dataset and focuses on human pose estimation. COCO-Pose includes multiple keypoints for each human instance.
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- **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses.
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- **Number of Classes**: 1 (Human).
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- **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
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- **Usage**: Suitable for training human pose estimation models.
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- **Additional Notes**: The dataset is rich and diverse, containing over 200k labeled images.
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- [Read more about COCO-Pose](./coco.md)
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### COCO8-Pose
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- **Description**: [Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation.
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- **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses.
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- **Number of Classes**: 1 (Human).
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- **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
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- **Usage**: Suitable for testing and debugging object detection models, or for experimenting with new detection approaches.
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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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### Conversion Tool
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Ultralytics provides a convenient conversion tool to convert labels from the popular COCO dataset format to YOLO format:
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!!! example ""
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=== "Python"
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```python
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from ultralytics.data.converter import convert_coco
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convert_coco(labels_dir='path/to/coco/annotations/', use_keypoints=True)
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```
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This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.
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---
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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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## 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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|
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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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|
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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
|
||||
|
||||
!!! example "Inference Example"
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||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load a model
|
||||
model = YOLO('path/to/best.pt') # load a tiger-pose trained model
|
||||
|
||||
# Run inference
|
||||
results = model.predict(source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True)
|
||||
```
|
||||
|
||||
=== "CLI"
|
||||
|
||||
```bash
|
||||
# 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).
|
||||
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