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COCO JSON

What is COCO JSON?

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.

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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:
‍

Free data conversion

SOC II Type 2 Compliant

Trusted by 250,000+ developers

Sagemaker GroundTruth Manifest

COCO JSON

Google Cloud AutoML Vision CSV

OpenAI CLIP Classification

COCO JSON

RetinaNet Keras CSV

COCO JSON

Tensorflow Object Detection CSV

COCO JSON

Multiclass Classification CSV

COCO JSON

YOLOv4 PyTorch TXT

COCO JSON

YOLOv5 Oriented Bounding Boxes

Convert Data from COCO JSON

Use Roboflow to convert the following formats to

COCO JSON

format.

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:
‍

Free data conversion

SOC II Type 1 Compliant

Trusted by 250,000+ developers

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

Once your data is in Roboflow, just add the link from your dataset and you're ready to go. We even include the code to export to common inference formats like TFLite, ONNX, and CoreML.

Below are pre-configured models that use the

COCO JSON

data format

What computer vision models use COCO JSON?

The

DETR

YOLOS

Mask RCNN

EfficientDet

Detectron2

models all use the

COCO JSON

data format.

DETR

Detection Transformer (DETR) is an end-to-end object detection model implemented using the Transformer architecture.

YOLOS

YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP.

Mask RCNN

Mask RCNN is a convolutional neural network for instance segmentation.

EfficientDet

EfficientDet achieves the best performance in the fewest training epochs among object detection model architectures, making it a highly scalable architecture especially when operating with limited compute.

Detectron2

Detectron2 is model zoo of it's own for computer vision models written in PyTorch.

To see our entire list of computer vision models, check out the Roboflow Model Library.

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
        }
    ]
}