---
comments: true
description: Learn about the Ultralytics YOLO dataset format for segmentation models. Use YAML to train Detection Models. Convert COCO to YOLO format using Python.
keywords: instance segmentation datasets, yolov8 segmentations, yaml dataset format, auto annotation, convert label formats
---

# 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:

```yaml
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:

```yaml
names:
  0: person
  1: bicycle
```

** Example **

```yaml
train: data/train/
val: data/val/

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

## Usage

!!! example ""

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

        # Train the model
        model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
        ```
    === "CLI"
    
        ```bash
        # Start training from a pretrained *.pt model
        yolo detect train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
        ```

## Supported Datasets

## 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_segments=True)
```

## Auto-Annotation

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:

```python
from ultralytics.yolo.data.annotator import auto_annotate

auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
```

| 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 'yolov8x.pt'.                                             | 'yolov8x.pt' |
| sam_model  | str, optional       | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'.                                             | 'sam_b.pt'   |
| 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](https://docs.ultralytics.com/models/sam), 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.