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Instance Segmentation Datasets Overview

Supported Dataset Formats

Ultralytics YOLO format

Label Format

The dataset format used for training YOLO segmentation 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 bounding coordinates: The bounding coordinates around the mask area, normalized to be between 0 and 1.

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

<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>

In this format, <class-index> is the index of the class for the object, and <x1> <y1> <x2> <y2> ... <xn> <yn> are the bounding coordinates of the object's segmentation mask. The coordinates are separated by spaces.

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

0 0.6812 0.48541 0.67 0.4875 0.67656 0.487 0.675 0.489 0.66
1 0.5046 0.0 0.5015 0.004 0.4984 0.00416 0.4937 0.010 0.492 0.0104

Note: The length of each row does not have to be equal.

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

Supported Datasets

Port or Convert label formats

COCO dataset format to YOLO format

from import convert_coco

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


Auto-annotation is an essential feature that allows you to generate a segmentation dataset using a pre-trained detection model. It enables you to quickly and accurately annotate a large number of images without the need for manual labeling, saving time and effort.

Generate Segmentation Dataset Using a Detection Model

To auto-annotate your dataset using the Ultralytics framework, you can use the auto_annotate function as shown below:

from import auto_annotate

auto_annotate(data="path/to/images", det_model="", sam_model='')
Argument Type Description Default
data str Path to a folder containing images to be annotated.
det_model str, optional Pre-trained YOLO detection model. Defaults to ''. ''
sam_model str, optional Pre-trained SAM segmentation model. Defaults to ''. ''
device str, optional Device to run the models on. Defaults to an empty string (CPU or GPU, if available).
output_dir str, None, optional Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. None

The auto_annotate function takes the path to your images, along with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results.

By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.

Created 2023-05-08, Updated 2023-05-17
Authors: Glenn Jocher (4)