MS COCO Object Detection

COCO export

Downloaded file: a zip archive with the structure described here

archive.zip/
├── images/
│   ├── train/
│   │   ├── <image_name1.ext>
│   │   ├── <image_name2.ext>
│   │   └── ...
│   └── val/
│       ├── <image_name1.ext>
│       ├── <image_name2.ext>
│       └── ...
└── annotations/
   ├── <task>_<subset_name>.json
   └── ...

If the dataset is exported from a Project, the subsets are named the same way as they are named in the project. In other cases there will be a single default subset, containing all the data. The <task> part corresponds to one of the COCO tasks: instances, person_keypoints, panoptic, image_info, labels, captions, stuff. There can be several annotation files in the archive.

  • supported annotations: Polygons, Rectangles
  • supported attributes:
    • is_crowd (checkbox or integer with values 0 and 1) - specifies that the instance (an object group) should have an RLE-encoded mask in the segmentation field. All the grouped shapes are merged into a single mask, the largest one defines all the object properties
    • score (number) - the annotation score field
    • arbitrary attributes - will be stored in the attributes annotation section

Support for COCO tasks via Datumaro is described here For example, support for COCO keypoints over Datumaro:

  1. Install Datumaro pip install datumaro
  2. Export the task in the Datumaro format, unzip
  3. Export the Datumaro project in coco / coco_person_keypoints formats datum export -f coco -p path/to/project [-- --save-images]

This way, one can export CVAT points as single keypoints or keypoint lists (without the visibility COCO flag).

COCO import

Uploaded file: a single unpacked *.json or a zip archive with the structure described above or here (without images).

  • supported annotations: Polygons, Rectangles (if the segmentation field is empty)
  • supported tasks: instances, person_keypoints (only segmentations will be imported), panoptic

MS COCO Keypoint Detection

COCO export

Downloaded file: a zip archive with the structure described here

  • supported annotations: Skeletons
  • supported attributes:
    • is_crowd (checkbox or integer with values 0 and 1) - specifies that the instance (an object group) should have an RLE-encoded mask in the segmentation field. All the grouped shapes are merged into a single mask, the largest one defines all the object properties
    • score (number) - the annotation score field
    • arbitrary attributes - will be stored in the attributes annotation section

COCO import

Uploaded file: a single unpacked *.json or a zip archive with the structure described here (without images).

  • supported annotations: Skeletons

How to create a task from MS COCO dataset

  1. Download the MS COCO dataset.

    For example val images and instances annotations

  2. Create a CVAT task with the following labels:

    person bicycle car motorcycle airplane bus train truck boat "traffic light" "fire hydrant" "stop sign" "parking meter" bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard "sports ball" kite "baseball bat" "baseball glove" skateboard surfboard "tennis racket" bottle "wine glass" cup fork knife spoon bowl banana apple sandwich orange broccoli carrot "hot dog" pizza donut cake chair couch "potted plant" bed "dining table" toilet tv laptop mouse remote keyboard "cell phone" microwave oven toaster sink refrigerator book clock vase scissors "teddy bear" "hair drier" toothbrush

  3. Select val2017.zip as data (See Creating an annotation task guide for details)

  4. Unpack annotations_trainval2017.zip

  5. click Upload annotation button, choose COCO 1.1 and select instances_val2017.json annotation file. It can take some time.