docs/en/datasets/segment/coco8-seg.md
Ultralytics COCO8-Seg is a small but versatile instance segmentation 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 segmentation 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.
labels/{train,val} matching each image file.Explore COCO8-Seg on Ultralytics Platform to browse every image with its polygon masks, view the class distribution and annotation heatmaps in the Charts tab, and clone it to train your own model in the cloud.
A YAML 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-Seg dataset, the coco8-seg.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml.
!!! example "ultralytics/cfg/datasets/coco8-seg.yaml"
```yaml
--8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
```
To train a YOLO26n-seg model on the COCO8-Seg 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 page.
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
```
Here are some examples of images from the COCO8-Seg dataset, along with their corresponding annotations:
If you use the COCO dataset in your research or development work, please cite the following paper:
!!! quote ""
=== "BibTeX"
```bibtex
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
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},
year={2015},
eprint={1405.0312},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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.
The COCO8-Seg dataset is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set (4 for training, 4 for validation). This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics YOLO26 for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model Training page.
To train a YOLO26n-seg model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n-seg.pt") # Load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo segment train data=coco8-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
```
For a thorough explanation of available arguments and configuration options, you can check the Training documentation.
Because the download and train/val loop are much smaller than full COCO, COCO8-Seg lets you run a training-and-validation pass to catch pipeline errors — a broken data loader, a misconfigured loss, or a bad augmentation — before committing to a larger dataset. Learn more about supported dataset formats in the Ultralytics segmentation dataset guide.
The YAML configuration file for the COCO8-Seg dataset is available in the Ultralytics repository. You can access the file directly at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml. The YAML file includes essential information about dataset paths, classes, and configuration settings required for model training and validation.
COCO8-Seg (8 images) sits below COCO128-Seg (128 images) and the full COCO-Seg dataset (118,287 training images) in terms of size:
Use COCO8-Seg for the fastest possible pipeline check, then scale to COCO128-Seg or the full COCO-Seg dataset as confidence grows.