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# COCO128 Dataset

## Introduction

Ultralytics COCO128 is a small, but versatile object detection dataset composed of the first 128 images of the COCO train 2017 set. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 128 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.

Watch: Ultralytics COCO Dataset Overview

This dataset is intended for use with Ultralytics HUB and YOLO11.

## Dataset YAML

A YAML (Yet Another Markup Language) 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 COCO128 dataset, the `coco128.yaml` file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco128.yaml.

ultralytics/cfg/datasets/coco128.yaml

    # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

    # COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
    # Documentation: https://docs.ultralytics.com/datasets/detect/coco/
    # Example usage: yolo train data=coco128.yaml
    # parent
    # ├── ultralytics
    # └── datasets
    #     └── coco128 ← downloads here (7 MB)

    # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
    path: coco128 # dataset root dir
    train: images/train2017 # train images (relative to 'path') 128 images
    val: images/train2017 # val images (relative to 'path') 128 images
    test: # test images (optional)

    # Classes
    names:
      0: person
      1: bicycle
      2: car
      3: motorcycle
      4: airplane
      5: bus
      6: train
      7: truck
      8: boat
      9: traffic light
      10: fire hydrant
      11: stop sign
      12: parking meter
      13: bench
      14: bird
      15: cat
      16: dog
      17: horse
      18: sheep
      19: cow
      20: elephant
      21: bear
      22: zebra
      23: giraffe
      24: backpack
      25: umbrella
      26: handbag
      27: tie
      28: suitcase
      29: frisbee
      30: skis
      31: snowboard
      32: sports ball
      33: kite
      34: baseball bat
      35: baseball glove
      36: skateboard
      37: surfboard
      38: tennis racket
      39: bottle
      40: wine glass
      41: cup
      42: fork
      43: knife
      44: spoon
      45: bowl
      46: banana
      47: apple
      48: sandwich
      49: orange
      50: broccoli
      51: carrot
      52: hot dog
      53: pizza
      54: donut
      55: cake
      56: chair
      57: couch
      58: potted plant
      59: bed
      60: dining table
      61: toilet
      62: tv
      63: laptop
      64: mouse
      65: remote
      66: keyboard
      67: cell phone
      68: microwave
      69: oven
      70: toaster
      71: sink
      72: refrigerator
      73: book
      74: clock
      75: vase
      76: scissors
      77: teddy bear
      78: hair drier
      79: toothbrush

    # Download script/URL (optional)
    download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip

## Usage

To train a YOLO11n model on the COCO128 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

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)

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

    # Start training from a pretrained *.pt model
    yolo detect train data=coco128.yaml model=yolo11n.pt epochs=100 imgsz=640

## Sample Images and Annotations

Here are some examples of images from the COCO128 dataset, along with their corresponding annotations:

- Mosaiced Image: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.

The example showcases the variety and complexity of the images in the COCO128 dataset and the benefits of using mosaicing during the training process.

## Citations and Acknowledgments

If you use the COCO dataset in your research or development work, please cite the following paper:

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.

## FAQ

### What is the Ultralytics COCO128 dataset used for?

The Ultralytics COCO128 dataset is a compact subset containing the first 128 images from the COCO train 2017 dataset. It's primarily used for testing and debugging object detection models, experimenting with new detection approaches, and validating training pipelines before scaling to larger datasets. Its manageable size makes it perfect for quick iterations while still providing enough diversity to be a meaningful test case.

### How do I train a YOLO11 model using the COCO128 dataset?

To train a YOLO11 model on the COCO128 dataset, you can use either Python or CLI commands. Here's how:

Train Example

Python CLI

    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)

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

For more training options and parameters, refer to the Training 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 (8 images) and the full COCO 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, consider using purpose-built variants like COCO8-seg which include the appropriate annotations.

📅 Created 6 months ago ✏️ Updated 6 months ago

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