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Object Detection Datasets Overview

Supported Dataset Formats

Ultralytics YOLO format

Label Format

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

  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.
  2. One row per object: Each row in the text file corresponds to one object instance in the image.
  3. 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.

The format for a single row in the detection dataset file is as follows:

<object-class> <x> <y> <width> <height>

Here is an example of the YOLO dataset format for a single image with two object instances:

0 0.5 0.4 0.3 0.6
1 0.3 0.7 0.4 0.2

In this example, the first object is of class 0 (person), with its center at (0.5, 0.4), width of 0.3, and height of 0.6. The second object is of class 1 (car), with its center at (0.3, 0.7), width of 0.4, and height of 0.2.

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>]

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:

  0: person
  1: bicycle


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

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


from ultralytics import YOLO

# Load a model
model = YOLO('')  # load a pretrained model (recommended for training)

# Train the model
model.train(data='coco128.yaml', epochs=100, imgsz=640)
# Start training from a pretrained *.pt model
yolo detect train data=coco128.yaml epochs=100 imgsz=640

Supported Datasets


Port or Convert label formats

COCO dataset format to YOLO format

from import convert_coco


Created 2023-05-08, Updated 2023-05-09
Authors: Glenn Jocher (2)