Datumaro Format

So far, in the field of computer vision, there are various tasks such as classification, detection, and segmentation, as well as pose estimation and visual tracking, and public data is used by providing a format suitable for each task. Even within the same segmentation task, some data formats provide annotation information as polygons, while others provide mask form. In order to ensure compatibility with different tasks and formats, we provide a novel Datumaro format with .json (Datumaro) or .datum (DatumaroBinary) extensions.

A variety of metadata can be stored in the datumaro format. First of all, dm_format_version field is provided for backward compatibility to help with data version tracing and various metadata can be added to the info field. For example, you can record task types such as detection and segmentation, or record data creation time. Labels and attributes can be saved in the categories field, and mask colormap information can be saved. In addition, in the datumaro format, in order to respond to hierarchical classification or multi-label classification tasks, label_group is provided to record whether or not enabling multiple selection between labels in a group and the parent is provided to specify the parent label for each label. Finally, in the item field, we can write the annotation information for each media id, and additionally write the data path and data size.

Here is the example of json annotation file:

{
    "dm_format_version": "1.0",
    "info": {
        "task": "anomaly_detection",
        "creation time": "2023.4.1"
    },
    "categories": {
        "label": {
            "labels": [
                {
                    "name": "Normal",
                    "parent": "",
                    "attributes": []
                },
                {
                    "name": "Anomalous",
                    "parent": "",
                    "attributes": []
                }
            ],
            "label_groups": [
                {
                    "name": "Label",
                    "group_type": "exclusive",
                    "labels": [
                        "Anomalous",
                        "Normal"
                    ]
                }
            ],
            "attributes": []
        },
        "mask": {
            "colormap": [
                {
                    "label_id": 1,
                    "r": 255,
                    "g": 255,
                    "b": 255
                }
            ]
        }
    },
    "items": [
        {
            "id": "good_001",
            "annotations": [
                {
                    "id": 0,
                    "type": "label",
                    "attributes": {},
                    "group": 0,
                    "label_id": 0
                }
            ],
            "image": {
                "path": "good_001.jpg",
                "size": [
                    900,
                    900
                ]
            }
        },
        {
            "id": "broken_small_001",
            "annotations": [
                {
                    "id": 0,
                    "type": "bbox",
                    "attributes": {},
                    "group": 0,
                    "label_id": 1,
                    "z_order": 0,
                    "bbox": [
                        350.8999938964844,
                        151.3899993896484,
                        275.1399841308594,
                        126.4900054931640
                    ]
                }
            ],
            "image": {
                "path": "broken_small_001.jpg",
                "size": [
                    900,
                    900
                ]
            }
        },
    ]
}

A Datumaro format directory have the following structure:

dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── images/
│   ├── train/  # directory with training images
│   |    ├── img001.png
│   |    ├── img002.png
│   |    └── ...
│   ├── val/  # directory with validation images
│   |    ├── img001.png
│   |    ├── img002.png
│   |    └── ...
│   └── ...
├── videos/  # directory to store video files
└── annotations/
    ├── train.json  # annotation file with training data
    ├── val.json  # annotation file with validation data
    └── ...