VGG Image Annotator JSON Annotation Format

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VGG Image Annotator JSON

VGG Image Annotator JSON

The Visual Geometry Group (VGG) at the University of Oxford released an open source annotation tool called VIA (VGG Image Annotator). It outputs its labels to CSV or JSON; Roboflow supports importing both and converting them to any other object detection format you like.

See our VGG Image Annotator Tutorial to learn how to use VIA to create object detection annotations.

VGG Image Annotator JSON

Use Roboflow to convert

VGG Image Annotator JSON

to the following formats.

VGG Image Annotator JSON

VGG Image Annotator JSON

Tensorflow TFRecord

VGG Image Annotator JSON

VGG Image Annotator JSON

YOLO Darknet TXT

VGG Image Annotator JSON

VGG Image Annotator JSON

YOLOv5 PyTorch TXT

VGG Image Annotator JSON

Google Cloud AutoML Vision CSV

VGG Image Annotator JSON

Sagemaker GroundTruth Manifest

VGG Image Annotator JSON

Scaled-YOLOv4 TXT

VGG Image Annotator JSON

VGG Image Annotator JSON

OpenAI CLIP Classification

VGG Image Annotator JSON

RetinaNet Keras CSV

VGG Image Annotator JSON

Tensorflow Object Detection CSV

VGG Image Annotator JSON

Multiclass Classification CSV

VGG Image Annotator JSON

YOLOv4 PyTorch TXT

Use Roboflow to convert the following formats to

VGG Image Annotator JSON

format.

Here are pre-configured models that use

VGG Image Annotator JSON

. 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.

We don't currently have models that use this annotation format.

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

via_export_json.json

{
    "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": {}
    }
}