Ultralytics COCO8 is a small, but versatile object detection 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 object detection 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.

To train a YOLOv8n model on the COCO8 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.

Train Example

# Start training from a pretrained *.pt model
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640

Explore the Ultralytics COCO8 dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.

Ultralytics COCO8-pose Dataset

Ultralytics COCO8-pose is a small, but versatile pose detection 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 object detection 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… See the full description on the dataset page: https://huggingface.co/datasets/Ultralytics/COCO8-pose.

Abdrah/coco8 dataset hosted on Hugging Face and contributed by the HF Datasets community

Coco128 Seg

## Overview

Coco128 Seg is a dataset for semantic segmentation tasks - it contains Laser annotations for 126 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).