Supported Formats

List of supported formats:

• 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
• PASCAL VOC (classification, detection, segmentation (class, instances), action_classification, person_layout)
  • Format specification
  • Dataset example
  • Format documentation
• YOLO (bboxes)
  • Format specification
  • Dataset example
  • Format documentation
• TF Detection API (bboxes, masks)
  • Format specifications: bboxes, masks
  • Dataset example
• WIDER Face (bboxes)
  • Format specification
  • Dataset example
• VGGFace2 (landmarks, bboxes)
  • Format specification
  • Dataset example
• MOT sequences
  • Format specification
  • Dataset example
• MOTS (png)
  • Format specification
  • Dataset example
• ImageNet (classification, detection)
  • Dataset example
  • Dataset example (txt for classification)
  • Detection format is the same as in PASCAL VOC
• CIFAR-10/100 (classification (python version))
  • Format specification
  • Dataset example CIFAR-10
  • Dataset example CIFAR-100
  • Format documentation
• MNIST (classification)
  • Format specification
  • Dataset example
  • Format documentation
• MNIST in CSV (classification)
  • Format specification
  • Dataset example
  • Format documentation
• CamVid (segmentation)
  • Format specification
  • Dataset example
• Cityscapes (segmentation)
  • Format specification
  • Dataset example
  • Format documentation
• KITTI (segmentation, detection)
  • Format specification
  • Dataset example
  • Format documentation
• KITTI 3D (raw/tracklets/velodyne points)
  • Format specification
  • Dataset example
  • Format documentation
• Supervisely (pointcloud)
  • Format specification
  • Dataset example
  • Format documentation
• SYNTHIA (segmentation)
  • Format specification
  • Dataset example
  • Format documentation
• CVAT
  • Format specification
  • Dataset example
• LabelMe
  • Format specification
  • Dataset example
• ICDAR13/15 (word_recognition, text_localization, text_segmentation)
  • Format specification
  • Dataset example
• Market-1501 (person re-identification)
  • Format specification
  • Dataset example
• LFW (classification, person re-identification, landmarks)
  • Format specification
  • Dataset example
• CelebA (classification, detection, landmarks)
  • Format specification
  • Dataset example
  • Format documentation
• Align CelebA (classification, landmarks)
  • Format specification
  • Dataset example
  • Format documentation
• VoTT CSV (detection)
  • Format specification
  • Dataset example
  • Format documentation
• VoTT JSON (detection)
  • Format specification
  • Dataset example
  • Format documentation
• MPII Human Pose Dataset (detection, pose estimation)
  • Format specification
  • Dataset example
  • Format documentation
• MPII Human Pose Dataset (JSON) (detection, pose estimation)
  • 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.