Supported Formats
List of supported formats:
- MS COCO
(
image_info,instances,person_keypoints,captions,labels,panoptic,stuff)- Format specification
- Dataset example
labelsare our extension - likeinstanceswith onlycategory_id- Format documentation
- PASCAL VOC (
classification,detection,segmentation(class, instances),action_classification,person_layout) - YOLO (
bboxes) - TF Detection API (
bboxes,masks)- Format specifications: bboxes, masks
- Dataset example
- WIDER Face (
bboxes) - VGGFace2 (
landmarks,bboxes) - MOT sequences
- MOTS (png)
- 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)) - MNIST (
classification) - MNIST in CSV (
classification) - CamVid (
segmentation) - Cityscapes (
segmentation) - KITTI (
segmentation,detection) - KITTI 3D (
raw/tracklets/velodyne points) - Supervisely (
pointcloud) - CVAT
- LabelMe
- ICDAR13/15 (
word_recognition,text_localization,text_segmentation) - Market-1501 (
person re-identification) - LFW (
classification,person re-identification,landmarks) - CelebA (
classification,detection,landmarks) - Align CelebA (
classification,landmarks)
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.