Overview

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.

Format Description

Below, learn the structure of COCO JSON.

coco.json

{
    "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
        }
    ]
}

Split and Merge Datasets

With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in COCO JSON. Read our dedicated guides to learn how to merge and split COCO JSON detections.

Split detections in COCO JSON


import supervision as sv

ds = sv.DetectionDataset.from_coco(
    images_directory_path=f"dataset1/train",
    annotations_path=f"dataset1/train/_annotations.coco.json",
)

train_ds, test_ds = ds.split(split_ratio=0.7,
                             random_state=42, shuffle=True)
len(train_ds), len(test_ds)
# (700, 300)

Merge detections in COCO JSON

No items found.

Supported Models

Below, see model architectures that require data in the COCO JSON format when training a new model.

On each page below, you can find links to our guides that show how to plot predictions from the model, and complete other common tasks like detecting small objects with the model.



COCO JSON

What is COCO JSON?

Not to worry, Roboflow is the universal tool for computer vision format conversion and can seamlessly input and output files in COCO JSON format.

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COCO JSON

Below, we show how to convert data to and from

COCO JSON

. We also list popular models that use the

COCO JSON

data format. Our conversion tools are free to use.

Convert Data to COCO JSON

Use Roboflow to convert

COCO JSON

to the following formats.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get a solution with:
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COCO JSON

Overview

Format Description

Split and Merge Datasets

Split detections in COCO JSON

Merge detections in COCO JSON

Supported Models

YOLOv8 Pose Estimation

DETR

YOLOS

Mask RCNN

EfficientDet

Detectron2

On this page

Convert Data from COCO JSON

Convert Data to COCO JSON

What is COCO JSON?

Deploy Models with Ease on Roboflow

COCO JSON

Convert Data to COCO JSON

Convert Data from COCO JSON

What computer vision models use COCO JSON?

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