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comments	description	keywords
true	Learn how to format your dataset for training YOLO models with Ultralytics YOLO format using our concise tutorial and example YAML files.	pose estimation, datasets, supported formats, YAML file, object class index, keypoints, ultralytics YOLO format

Pose Estimation Datasets Overview

Supported Dataset Formats

Ultralytics YOLO format

** Label Format **

The dataset format used for training YOLO segmentation models is as follows:

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.
One row per object: Each row in the text file corresponds to one object instance in the image.
Object information per row: Each row contains the following information about the object instance:
- Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
- Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
- Object width and height: The width and height of the object, normalized to be between 0 and 1.
- Object keypoint coordinates: The keypoints of the object, normalized to be between 0 and 1.

Here is an example of the label format for pose estimation task:

Format with Dim = 2

<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>

Format with Dim = 3

<class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <p2-visibility>

In this format, <class-index> is the index of the class for the object,<x> <y> <width> <height> are coordinates of boudning box, and <px1> <py1> <px2> <py2> ... <pxn> <pyn> are the pixel coordinates of the keypoints. The coordinates are separated by spaces.

** Dataset file format **

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:

train: <path-to-training-images>
val: <path-to-validation-images>

nc: <number-of-classes>
names: [<class-1>, <class-2>, ..., <class-n>]

# Keypoints
kpt_shape: [num_kpts, dim]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
flip_idx: [n1, n2 ... , n(num_kpts)]

The train and val fields specify the paths to the directories containing the training and validation images, respectively.

The nc field specifies the number of object classes in the dataset.

The names field is a list of the names of the object classes. The order of the names should match the order of the object class indices in the YOLO dataset files.

NOTE: Either nc or names must be defined. Defining both are not mandatory

Alternatively, you can directly define class names like this:

names:
  0: person
  1: bicycle

(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face. For example let's say there're five keypoints of facial landmark: [left eye, right eye, nose, left point of mouth, right point of mouse], 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)

** Example **

train: data/train/
val: data/val/

nc: 2
names: ['person', 'car']

# Keypoints
kpt_shape: [17, 3]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]

Usage

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)

    # Train the model
    model.train(data='coco128-pose.yaml', epochs=100, imgsz=640)
    ```
=== "CLI"

    ```bash
    # Start training from a pretrained *.pt model
    yolo detect train data=coco128-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
    ```

Supported Datasets

TODO

Port or Convert label formats

COCO dataset format to YOLO format

from ultralytics.yolo.data.converter import convert_coco

convert_coco(labels_dir='../coco/annotations/', use_keypoints=True)

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