Convert annotation formats

How To Convert VGG Image Annotator JSON to COCO JSON

In this guide, we show you how to convert data between the VIA JSON and COCO formats for free. You can use your converted data to train YOLOv8 Pose Estimation models and other models that support the COCO format.

Convert with an AI agent

Paste this prompt into your coding agent

Claude Code, Cursor, Codex or any agent with the Roboflow MCP uploads your VGG Image Annotator JSON dataset to Roboflow, generates a version and downloads it as COCO JSON.

No Roboflow MCP yet? The prompt has your agent set it up first, or connect your agent before you paste it.

Rather convert it by hand? Follow the steps below

VGG Image Annotator JSON

The VGG Image Annotator tool's JSON format.

Below, learn the structure of VGG Image Annotator JSON.

via_export_json.jsonJSON
{
    "img0001": {
        "filename": "img0001.png",
        "size": 2512968,
        "regions": [{
            "shape_attributes": {
                "name": "rect",
                "x": 827,
                "y": 890,
                "width": 150,
                "height": 651
            },
            "region_attributes": {
                "type": "helmet"
            }
        }, {
            "shape_attributes": {
                "name": "rect",
                "x": 1943,
                "y": 875,
                "width": 120,
                "height": 639
            },
            "region_attributes": {
                "type": "head"
            }
        }],
        "file_attributes": {}
    }
}

COCO JSON

COCO is a common JSON format used for machine learning because the dataset it was introduced with has become a common benchmark.

Below, learn the structure of COCO JSON.

coco.jsonJSON
{
    "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
        }
    ]
}

Step 1: Create a free Roboflow account

Roboflow is a universal conversion tool for computer vision annotation formats. To convert your dataset, start by creating a free Roboflow account.

Create free Roboflow account

Step 2: Upload your data into Roboflow

Once your account has been created, click Create New Project. (opens in a new tab)

Roboflow's create project form, with Object Detection selected as the project type

Upload your data to Roboflow by dragging and dropping your VGG Image Annotator JSON images and annotations into the upload space.

Roboflow's upload page, with a drag-and-drop area for images, annotations, videos and PDFs

Step 3: Generate Dataset Version

Next, click "Generate New Version" to generate a new version of your dataset:

Roboflow's Generate a Dataset Version page, showing the source images and train/test split for a new version

You can then apply any preprocessing or augmentation steps to your dataset:

The augmentation step of a new dataset version, with 90° Rotate and Brightness added

Step 4: Export Dataset Version

After generating, you will be prompted to Export your dataset. You can choose to receive your dataset as a .zip file or a curl download link. Choose COCO JSON when asked in what format you want to export your data. You will see a dropdown with various options like this:

Roboflow's Download dialog, with the export format list open and COCO highlighted

Congratulations, you have successfully converted your dataset from VGG Image Annotator JSON format to COCO JSON format!

Video Tutorial

Convert Computer Vision Annotation Formats Tutorial

Want to dive deeper into converting annotation formats with Roboflow? In the tutorial below, we explore how to convert your data in the Roboflow dashboard. We also discuss rejecting annotations and train-test-validation splits.

Frequently Asked Questions

Yes! It is free to convert VGG Image Annotator JSON data into the COCO JSON format on the Roboflow platform.

If you have between a few and a few thousand images, converting data between these formats will be quick. But, the time it takes to convert between data formats increases with the more images you have.

Show guide

Convert VGG Image Annotator JSON to other formats

Convert other formats to COCO JSON

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