Microsoft released the MS COCO dataset in 2015. It has become a common benchmark dataset for object detection models since then which has popularized the use of its JSON annotation format. You can learn how to create COCO JSON from scratch in our CVAT tutorial.
Unfortunately, COCO format is not anywhere near universal and so you may find yourself needing to convert it to another format for a model (or export to COCO JSON from another format if you happen to be using a model that supports it).
Not to worry, Roboflow is the universal tool for computer vision format conversion and can seamlessly input and output files in COCO JSON format.
With Roboflow, you can deploy a computer vision model without having to build your own infrastructure.
The
DETR
,
YOLOS
,
Mask RCNN
,
EfficientDet
,
Detectron2
,
models all use the
data format.
{
"info": {
"year": "2020",
"version": "1",
"description": "Exported from roboflow.ai",
"contributor": "Roboflow",
"url": "https://app.roboflow.ai/datasets/hard-hat-sample/1",
"date_created": "2000-01-01T00:00:00+00:00"
},
"licenses": [
{
"id": 1,
"url": "https://creativecommons.org/publicdomain/zero/1.0/",
"name": "Public Domain"
}
],
"categories": [
{
"id": 0,
"name": "Workers",
"supercategory": "none"
},
{
"id": 1,
"name": "head",
"supercategory": "Workers"
},
{
"id": 2,
"name": "helmet",
"supercategory": "Workers"
},
{
"id": 3,
"name": "person",
"supercategory": "Workers"
}
],
"images": [
{
"id": 0,
"license": 1,
"file_name": "0001.jpg",
"height": 275,
"width": 490,
"date_captured": "2020-07-20T19:39:26+00:00"
}
],
"annotations": [
{
"id": 0,
"image_id": 0,
"category_id": 2,
"bbox": [
45,
2,
85,
85
],
"area": 7225,
"segmentation": [],
"iscrowd": 0
},
{
"id": 1,
"image_id": 0,
"category_id": 2,
"bbox": [
324,
29,
72,
81
],
"area": 5832,
"segmentation": [],
"iscrowd": 0
}
]
}