COCO12-Formats Dataset

Introduction

The Ultralytics COCO12-Formats dataset is a specialized test dataset designed to validate image loading across 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 common image 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

When you manage datasets on Ultralytics Platform, images in any of these formats are handled automatically—no manual conversion required.

Supported Formats

The dataset includes one image for each of 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

No manual setup is needed. OpenCV reads most formats directly, and Ultralytics falls back to Pillow for AVIF, HEIC, and HEIF. AVIF is decoded natively by Pillow 11.3 or newer, so upgrade Pillow if AVIF files fail to load, and HEIC/HEIF decoding installs the lightweight pi-heif package automatically on first use.

Usage

The COCO12-Formats dataset (1 MB) downloads automatically the first time you start training. 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 12 supported 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. Ultralytics decodes it through Pillow 11.3 or newer when OpenCV lacks AVIF support, with no extra packages required.

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 / HEIF (High Efficiency Image File Format)

Both .heic and .heif extensions reference the same ISO/IEC 23008-12 container. By convention, .heic denotes HEVC-encoded HEIF files (the variant produced by Apple devices), while .heif is the broader umbrella extension.

Ultralytics decodes both via the OpenCV → Pillow fallback in ultralytics/utils/patches.py, which auto-installs pi-heif (lightweight, decode-only) on first use — no manual setup required for reading. To produce HEIC/HEIF files yourself (e.g., re-encoding sample images), install the full pillow-heif package, which includes encoders:

pip install pillow-heif

Use Cases

CI/CD Testing

from ultralytics import YOLO


def test_image_formats():
    """Test that 12 supported 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 dataset formats are supported
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"Unsupported formats: {found_formats - IMG_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 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?

None need manual installation. AVIF is decoded natively by Pillow 11.3 or newer, and HEIC/HEIF decoding installs pi-heif automatically on first use. Install the full pillow-heif package only if you need to write HEIC/HEIF files yourself.