Supported Formats

List of supported formats:

• ADE20k (v2017) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• ADE20k (v2020) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• Align CelebA (classification, landmarks) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• CamVid (segmentation)

  • Format specification

  • Dataset example

• CelebA (classification, detection, landmarks) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• CIFAR-10/100 (classification (python version))

  • Format specification

  • Dataset example CIFAR-10

  • Dataset example CIFAR-100

  • Format documentation

• Cityscapes (segmentation)

  • Format specification

  • Dataset example

  • Format documentation

• CVAT (for images, for video (import-only))

  • Format specification

  • Dataset example

• ICDAR13/15 (word_recognition, text_localization, text_segmentation)

  • Format specification

  • Dataset example

• ImageNet (classification, detection)

  • Dataset example

  • Dataset example (txt for classification)

  • Detection format is the same as in PASCAL VOC

  • Format documentation

• KITTI (segmentation, detection)

  • Format specification

  • Dataset example

  • Format documentation

• KITTI 3D (raw/tracklets/velodyne points)

  • Format specification

  • Dataset example

  • Format documentation

• LabelMe (labels, boxes, masks)

  • Format specification

  • Dataset example

• LFW (classification, person re-identification, landmarks)

  • Format specification

  • Dataset example

  • Format documentation

• Mapillary Vistas (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• Market-1501 (person re-identification)

  • Format specification

  • Dataset example

• MARS (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• MNIST (classification)

  • Format specification

  • Dataset example

  • Format documentation

• MNIST in CSV (classification)

  • Format specification

  • Dataset example

  • Format documentation

• MOT sequences

  • Format specification

  • Dataset example

• MOTS (png)

  • Format specification

  • Dataset example

• MPII Human Pose Dataset (detection, pose estimation) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• MPII Human Pose Dataset (JSON) (detection, pose estimation) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• MS COCO (image_info, instances, person_keypoints, captions, labels,panoptic, stuff)

  • Format specification

  • Dataset example

  • labels are our extension - like instances with only category_id

  • Format documentation

• Open Images (classification, detection, segmentation)

  • Format specification

  • Dataset example

  • Format documentation

• PASCAL VOC (classification, detection, segmentation (class, instances), action_classification, person_layout)

  • Format specification

  • Dataset example

  • Format documentation

• Supervisely (pointcloud)

  • Format specification

  • Dataset example

  • Format documentation

• SYNTHIA (segmentation) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• TF Detection API (bboxes, masks)

  • Format specifications: bboxes, masks

  • Dataset example

• VGGFace2 (landmarks, bboxes)

  • Format specification

  • Dataset example

  • Format documentation

• VoTT CSV (detection) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• VoTT JSON (detection) (import-only)

  • Format specification

  • Dataset example

  • Format documentation

• WIDER Face (bboxes)

  • Format specification

  • Dataset example

  • Format documentation

• YOLO (bboxes)

  • Format specification

  • Dataset example

  • Format documentation

Supported annotation types

• Labels

• Bounding boxes

• Polygons

• Polylines

• (Segmentation) Masks

• (Key-)Points

• Captions

• 3D cuboids

Datumaro does not separate datasets by tasks like classification, detection etc. Instead, datasets can have any annotations. When a dataset is exported in a specific format, only relevant annotations are exported.

Dataset meta info file

It is possible to use classes that are not original to the format. To do this, use dataset_meta.json.

{
"label_map": {"0": "background", "1": "car", "2": "person"},
"segmentation_colors": [[0, 0, 0], [255, 0, 0], [0, 0, 255]],
"background_label": "0"
}

• label_map is a dictionary where the class ID is the key and the class name is the value.

• segmentation_colors is a list of channel-wise values for each class. This is only necessary for the segmentation task.

• background_label is a background label ID in the dataset.