Formats
VGG Image Annotator JSON
Object Detection
IMPORTEXPORT
FEATURED
Object Detection

VGG Image Annotator JSON

The VGG Image Annotator tool's JSON format.

Overview

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.

Format Description

Below, learn the structure of VGG Image Annotator JSON.

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": {}
    }
}

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 VGG Image Annotator JSON. Read our dedicated guides to learn how to merge and split VGG Image Annotator JSON detections.

Split detections in VGG Image Annotator JSON

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Merge detections in VGG Image Annotator JSON

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Supported Models

Below, see model architectures that require data in the VGG Image Annotator 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.

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On this page

Convert Data from VGG Image Annotator JSON

Convert to...

Convert Data to VGG Image Annotator JSON

Convert from...
Formats
VGG Image Annotator JSON

What is 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.

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With Roboflow, you can deploy a computer vision model without having to build your own infrastructure.

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VGG Image Annotator JSON

Below, we show how to convert data to and from

VGG Image Annotator JSON

. We also list popular models that use the

VGG Image Annotator JSON

data format. Our conversion tools are free to use.

CONVERT To
CONVERT From
EXAMPLE

Convert Data to VGG Image Annotator JSON

Use Roboflow to convert
VGG Image Annotator 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

VGG Image Annotator JSON
 to 
YOLOv8 PyTorch TXT
VGG Image Annotator JSON
 to 
YOLOv8 Oriented Bounding Boxes
VGG Image Annotator JSON
 to 
COCO JSON
VGG Image Annotator JSON
 to 
Tensorflow TFRecord
VGG Image Annotator JSON
 to 
Pascal VOC XML
VGG Image Annotator JSON
 to 
CreateML JSON
VGG Image Annotator JSON
 to 
YOLO Darknet TXT
VGG Image Annotator JSON
 to 
YOLOv7 PyTorch TXT
VGG Image Annotator JSON
 to 
YOLOv5 PyTorch TXT
VGG Image Annotator JSON
 to 
Sagemaker GroundTruth Manifest
VGG Image Annotator JSON
 to 
Google Cloud AutoML Vision CSV
VGG Image Annotator JSON
 to 
meituan/yolov6
VGG Image Annotator JSON
 to 
Scaled-YOLOv4 TXT
VGG Image Annotator JSON
 to 
YOLO Keras TXT
VGG Image Annotator JSON
 to 
OpenAI CLIP Classification
VGG Image Annotator JSON
 to 
RetinaNet Keras CSV
VGG Image Annotator JSON
 to 
Tensorflow Object Detection CSV
VGG Image Annotator JSON
 to 
Multiclass Classification CSV
VGG Image Annotator JSON
 to 
YOLOv4 PyTorch TXT
VGG Image Annotator JSON
 to 
YOLOv5 Oriented Bounding Boxes

Convert Data from VGG Image Annotator JSON

Use Roboflow to convert the following formats to
VGG Image Annotator 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
VGG Image Annotator JSON
data format
.

What computer vision models use VGG Image Annotator JSON?

The

No items found.

models all use the

VGG Image Annotator JSON

data format.

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": {}
    }
}
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