COCO Dataset format

Hasty allows you to export your project in the very well-known COCO dataset format. You can find more information about this format here.

COCO Masks Decoding Python Example


      #Decode binary masks encoded via RLE.
from pycocotools.mask import decode

ann = {
    "id": 0,
    "image_id": 53,
    "category_id": 1,
    "segmentation": {
    "size": [ 
    2211,
    2097
    ],
    "counts": "WRQR14oT20K?D5M0O7I6K3M005K3M004L0N4N001O02N001N2O0001O2N00001O02N0000N3O0000002N00001O000O1O1000000000001O00000N20000000O01000000000000N2000O10O1000N20O10000N200000O01000000N11000000000000N20000000000O3N00001O2N001O0N3O002M2O002M3L7J101H>A_SbS3"
    },  
    "area": 6718,
    "bbox": [
    504,
    842,
    117,
    75
    ],
    "iscrowd": 0
}

mask = decode(ann["segmentation"])
    

The Annotation Object (“ann”)

Attributes

id (string)
The annotation ID

image_id (integer)
The ID of the image

category_id (integer)
The ID of the category of the object instance

segmentation

• size (list of integers)
  Height and width of the image
• counts (string)
  An RLE-encoded binary mask of an object instance

area (float)
The total area of the encoded mask in squared pixels

bbox (list of integers)
List of bounding box values

iscrowd (boolean)
Indicates if the ground truth (gt) object is marked as a crowd region (1) or not (0). If it is, then the predicted bounded box can match any subregion of the ground truth. Currently, Hasty does not provide an option to mark objects as iscrowd, so the value will be set as 0

You can learn more about pycocotools Python package in the documentation.