COCO12-Formats Dataset

Introduction

The Ultralytics COCO12-Formats dataset is a specialized test dataset designed to validate image loading across all 12 supported image format extensions. It contains 12 images (6 for training, 6 for validation), each saved in a different format to ensure comprehensive testing of the image loading pipeline.

This dataset is invaluable for:

• Testing image format support: Verify that all supported formats load correctly
• CI/CD pipelines: Automated testing of format compatibility
• Debugging: Isolate format-specific issues in training pipelines
• Development: Validate new format additions or changes

Supported Formats

The dataset includes one image for each of the 12 supported format extensions defined in ultralytics/data/utils.py:

Dataset Structure

coco12-formats/
├── images/
│   ├── train/          # 6 images (avif, bmp, dng, heic, jpeg, jpg)
│   └── val/            # 6 images (jp2, mpo, png, tif, tiff, webp)
├── labels/
│   ├── train/          # Corresponding YOLO format labels
│   └── val/
└── coco12-formats.yaml # Dataset configuration

Dataset YAML

The COCO12-Formats dataset is configured using a YAML file that defines dataset paths and class names. You can review the official coco12-formats.yaml file in the Ultralytics GitHub repository.

!!! example "ultralytics/cfg/datasets/coco12-formats.yaml"

```yaml
--8<-- "ultralytics/cfg/datasets/coco12-formats.yaml"
```

Requirements

Some formats require additional dependencies:

pip install pillow pillow-heif pillow-avif-plugin

AVIF System Library (Optional)

For OpenCV to read AVIF files directly, libavif must be installed before building OpenCV:

=== "macOS"

```bash
brew install libavif
```

=== "Ubuntu/Debian"

```bash
sudo apt install libavif-dev libavif-bin
```

=== "From Source"

```bash
git clone -b v1.2.1 https://github.com/AOMediaCodec/libavif.git
cd libavif
cmake -B build -DAVIF_CODEC_AOM=SYSTEM -DAVIF_BUILD_APPS=ON
cmake --build build --config Release --parallel
sudo cmake --install build
```

!!! note

The pip-installed `opencv-python` package may not include AVIF support since it's pre-built. Ultralytics uses Pillow with `pillow-avif-plugin` as a fallback for AVIF images when OpenCV lacks support.

Usage

To train a YOLO model on the COCO12-Formats dataset, use the following examples:

!!! example "Train Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLO model
    model = YOLO("yolo26n.pt")

    # Train on COCO12-Formats to test all image formats
    results = model.train(data="coco12-formats.yaml", epochs=1, imgsz=640)
    ```

=== "CLI"

    ```bash
    # Train YOLO on COCO12-Formats
    yolo detect train data=coco12-formats.yaml model=yolo26n.pt epochs=1 imgsz=640
    ```

Format-Specific Notes

AVIF (AV1 Image File Format)

AVIF is a modern image format based on the AV1 video codec, offering excellent compression. Requires pillow-avif-plugin:

pip install pillow-avif-plugin

DNG (Digital Negative)

DNG is Adobe's open RAW format based on TIFF. For testing purposes, the dataset uses TIFF-based files with the .dng extension.

JP2 (JPEG 2000)

JPEG 2000 is a wavelet-based image compression standard offering better compression and quality than traditional JPEG. Commonly used in medical imaging (DICOM), geospatial applications, and digital cinema. Natively supported by both OpenCV and Pillow.

MPO (Multi-Picture Object)

MPO files are used for stereoscopic (3D) images. The dataset stores standard JPEG data with the .mpo extension for format testing.

HEIC (High Efficiency Image Coding)

HEIC requires the pillow-heif package for proper encoding:

pip install pillow-heif

Use Cases

CI/CD Testing

from ultralytics import YOLO


def test_all_image_formats():
    """Test that all image formats load correctly."""
    model = YOLO("yolo26n.pt")
    results = model.train(data="coco12-formats.yaml", epochs=1, imgsz=64)
    assert results is not None

Format Validation

from pathlib import Path

from ultralytics.data.utils import IMG_FORMATS

# Verify all formats are represented
dataset_dir = Path("datasets/coco12-formats/images")
found_formats = {f.suffix[1:].lower() for f in dataset_dir.rglob("*.*")}
assert found_formats == IMG_FORMATS, f"Missing formats: {IMG_FORMATS - found_formats}"

Citations and Acknowledgments

If you use the COCO dataset in your research, please cite:

!!! 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 Doll{\'a}r},
          year={2015},
          eprint={1405.0312},
          archivePrefix={arXiv},
          primaryClass={cs.CV}
    }
    ```

FAQ

What Is the COCO12-Formats Dataset Used For?

The COCO12-Formats dataset is designed for testing image format compatibility in Ultralytics YOLO training pipelines. It ensures all 12 supported image formats (AVIF, BMP, DNG, HEIC, JP2, JPEG, JPG, MPO, PNG, TIF, TIFF, WebP) load and process correctly.

Why Test Multiple Image Formats?

Different image formats have unique characteristics (compression, bit depth, color spaces). Testing all formats ensures:

• Robust image loading code
• Compatibility across diverse datasets
• Early detection of format-specific bugs

Which Formats Require Special Dependencies?

• AVIF: Requires pillow-avif-plugin
• HEIC: Requires pillow-heif