مجموعة بيانات COCO128

from ultralytics import YOLO

    # Load a pretrained model
    model = YOLO("yolo11n.pt")

    # Train the model
    results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
    ```

=== "CLI"

`bash
    yolo detect train data=coco128.yaml model=yolo11n.pt epochs=100 imgsz=640
    `

For more training options and parameters, refer to the [Training](../../modes/train.md) documentation.

### What are the benefits of using mosaic augmentation with COCO128?

Mosaic augmentation, as shown in the sample images, combines multiple training images into a single composite image. This technique offers several benefits when training with COCO128:

- Increases the variety of objects and contexts within each training batch
- Improves model generalization across different object sizes and aspect ratios
- Enhances detection performance for objects at various scales
- Maximizes the utility of a small dataset by creating more diverse training samples

This technique is particularly valuable for smaller datasets like COCO128, helping models learn more robust features from limited data.

### How does COCO128 compare to other COCO dataset variants?

COCO128 (128 images) sits between [COCO8](../detect/coco8.md) (8 images) and the full [COCO](../detect/coco.md) dataset (118K+ images) in terms of size:

- **COCO8**: Contains just 8 images (4 train, 4 val) - ideal for quick tests and debugging
- **COCO128**: Contains 128 images - balanced between size and diversity
- **Full COCO**: Contains 118K+ training images - comprehensive but resource-intensive

COCO128 provides a good middle ground, offering more diversity than COCO8 while remaining much more manageable than the full COCO dataset for experimentation and initial model development.

### Can I use COCO128 for tasks other than object detection?

While COCO128 is primarily designed for object detection, the dataset's annotations can be adapted for other computer vision tasks:

- **Instance segmentation**: Using the segmentation masks provided in the annotations
- **Keypoint detection**: For images containing people with keypoint annotations
- **Transfer learning**: As a starting point for fine-tuning models for custom tasks

For specialized tasks like [segmentation](../../tasks/segment.md), consider using purpose-built variants like [COCO8-seg](../segment/coco8-seg.md) which include the appropriate annotations.