MMDetection COCO

Format specification

MMDetection is a training framework for object detection and instance segmentation tasks, providing a modular and flexible architecture that supports various state-of-the-art models, datasets, and training techniques. MMDetection has gained popularity in the research community for its comprehensive features and ease of use in developing and benchmarking object detection algorithms. MMDetection specifies their COCO format here.

Most of available tasks or formats are similar to the original COCO format, while only the image directories are separated with respect to subsets. In this document, we just describe the directory structure of MMDetection COCO format as per here. MMDetection COCO dataset directory should have the following structure:

└─ Dataset/
    ├── <subset_name>/
    │   ├── <image_name1.ext>
    │   ├── <image_name2.ext>
    │   └── ...
    ├── <subset_name>/
    │   ├── <image_name1.ext>
    │   ├── <image_name2.ext>
    │   └── ...
    └── annotations/
        ├── instances_<subset_name>.json
        └── ...

Import using CLI

datum project create
datum project import --format mmdet_coco <path/to/dataset>

Import using Python API

import datumaro as dm

dataset = dm.Dataset.import_from('<path/to/dataset>', 'mmdet_coco')