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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: ../datasets/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

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:

Dataset sample image

  • 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:

@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:

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.
📅 Created 8 days ago ✏️ Updated 0 days ago

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