Formats
SuperAnnotate JSON
Object Detection
IMPORTEXPORT
FEATURED
Object Detection

SuperAnnotate JSON

SuperAnnotate (formerly annotate.online) is a self-service labeling tool and outsourced annotation provider.

Overview

SuperAnnotate is a provider of outsourced labeling annotation services and develops a self-serve labeling tool (formerly known as annotate.online) which is available for download on Windows, Linux, and Mac. Their tool has support for advanced labeling functions like video frame annotation and using a trained model to pre-suggest annotations.

You can use Roboflow to convert SuperAnnotate's custom JSON format to any format your computer vision models expect.

Format Description

Below, learn the structure of SuperAnnotate JSON.

annotations.json
{
    "mask.jpg": [{
        "type": "bbox",
        "classId": 1,
        "probability": 100,
        "points": {
            "x1": 67.8,
            "x2": 187.6,
            "y1": 166.6,
            "y2": 274.1
        },
        "groupId": 0,
        "pointLabels": {},
        "locked": false,
        "visible": true,
        "attributes": []
    }, {
        "type": "polyline",
        "classId": 2,
        "probability": 100,
        "points": [148.7, 321.1, 164.5, 328.3, 220.4, 335.5, 280.6, 344.8, 330.1, 341.9, 373.1, 330.5, 375.3, 336.2, 377.4, 369.9, 382.4, 384.9, 377.4, 409.3, 360.2, 425.1, 318.6, 451.6, 279.9, 473.8, 262.7, 488.2, 252, 499.6, 241.9, 502.5, 223.3, 496.8, 210.4, 486, 181.7, 453.8, 165.9, 443, 155.9, 431.5, 148, 419.4, 132.3, 410, 123.7, 397.8, 139.4, 346.2, 151.6, 319.7, 150.9, 321.1],
        "groupId": 0,
        "pointLabels": {},
        "locked": false,
        "visible": true,
        "attributes": []
    }, {
        "type": "polygon",
        "classId": 2,
        "probability": 100,
        "points": [493.7, 355.6, 506.1, 366.1, 567.3, 384.2, 648.5, 352.7, 649.5, 372.8, 659, 386.1, 649.5, 402.4, 631.3, 424.4, 618.9, 451.1, 612.2, 456.9, 585.4, 474.1, 570.1, 477.9, 552, 466.4, 527.1, 454, 491.8, 402.4, 494.6, 377.5, 493.7, 355.6],
        "groupId": 0,
        "pointLabels": {},
        "locked": false,
        "visible": true,
        "attributes": []
    }, {
        "type": "meta",
        "name": "lastAction",
        "timestamp": 1600628979793
    }]
}
classes.json
[{
    "attribute_groups": [],
    "color": "#32a852",
    "id": 1,
    "name": "no-mask",
    "opened": true
}, {
    "attribute_groups": [],
    "color": "#d6d619",
    "id": 2,
    "name": "mask",
    "opened": true
}]

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 SuperAnnotate JSON. Read our dedicated guides to learn how to merge and split SuperAnnotate JSON detections.

Split detections in SuperAnnotate JSON

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Merge detections in SuperAnnotate JSON

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Supported Models

Below, see model architectures that require data in the SuperAnnotate 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.

No items found.

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Convert Data from SuperAnnotate JSON

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Formats
SuperAnnotate JSON

What is SuperAnnotate JSON?

SuperAnnotate is a provider of outsourced labeling annotation services and develops a self-serve labeling tool (formerly known as annotate.online) which is available for download on Windows, Linux, and Mac. Their tool has support for advanced labeling functions like video frame annotation and using a trained model to pre-suggest annotations.

You can use Roboflow to convert SuperAnnotate's custom JSON format to any format your computer vision models expect.

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

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SuperAnnotate JSON

Below, we show how to convert data to and from

SuperAnnotate JSON

. We also list popular models that use the

SuperAnnotate JSON

data format. Our conversion tools are free to use.

CONVERT To
CONVERT From
EXAMPLE

Convert Data to SuperAnnotate JSON

Use Roboflow to convert
SuperAnnotate 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

SuperAnnotate JSON
 to 
YOLOv8 PyTorch TXT
SuperAnnotate JSON
 to 
YOLOv8 Oriented Bounding Boxes
SuperAnnotate JSON
 to 
PaliGemma JSONL
SuperAnnotate JSON
 to 
YOLOv10 PyTorch TXT
SuperAnnotate JSON
 to 
YOLOv9 PyTorch TXT
SuperAnnotate JSON
 to 
Florence-2
SuperAnnotate JSON
 to 
COCO JSON
SuperAnnotate JSON
 to 
Pascal VOC XML
SuperAnnotate JSON
 to 
Tensorflow TFRecord
SuperAnnotate JSON
 to 
YOLO Darknet TXT
SuperAnnotate JSON
 to 
CreateML JSON
SuperAnnotate JSON
 to 
YOLOv7 PyTorch TXT
SuperAnnotate JSON
 to 
YOLOv5 PyTorch TXT
SuperAnnotate JSON
 to 
Sagemaker GroundTruth Manifest
SuperAnnotate JSON
 to 
Google Cloud AutoML Vision CSV
SuperAnnotate JSON
 to 
meituan/yolov6
SuperAnnotate JSON
 to 
Scaled-YOLOv4 TXT
SuperAnnotate JSON
 to 
YOLO Keras TXT
SuperAnnotate JSON
 to 
OpenAI CLIP Classification
SuperAnnotate JSON
 to 
RetinaNet Keras CSV
SuperAnnotate JSON
 to 
Tensorflow Object Detection CSV
SuperAnnotate JSON
 to 
Multiclass Classification CSV
SuperAnnotate JSON
 to 
YOLOv4 PyTorch TXT
SuperAnnotate JSON
 to 
YOLOv5 Oriented Bounding Boxes

Convert Data from SuperAnnotate JSON

Use Roboflow to convert the following formats to
SuperAnnotate 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
SuperAnnotate JSON
data format
.

What computer vision models use SuperAnnotate JSON?

The

No items found.

models all use the

SuperAnnotate 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.
annotations.json
{
    "mask.jpg": [{
        "type": "bbox",
        "classId": 1,
        "probability": 100,
        "points": {
            "x1": 67.8,
            "x2": 187.6,
            "y1": 166.6,
            "y2": 274.1
        },
        "groupId": 0,
        "pointLabels": {},
        "locked": false,
        "visible": true,
        "attributes": []
    }, {
        "type": "polyline",
        "classId": 2,
        "probability": 100,
        "points": [148.7, 321.1, 164.5, 328.3, 220.4, 335.5, 280.6, 344.8, 330.1, 341.9, 373.1, 330.5, 375.3, 336.2, 377.4, 369.9, 382.4, 384.9, 377.4, 409.3, 360.2, 425.1, 318.6, 451.6, 279.9, 473.8, 262.7, 488.2, 252, 499.6, 241.9, 502.5, 223.3, 496.8, 210.4, 486, 181.7, 453.8, 165.9, 443, 155.9, 431.5, 148, 419.4, 132.3, 410, 123.7, 397.8, 139.4, 346.2, 151.6, 319.7, 150.9, 321.1],
        "groupId": 0,
        "pointLabels": {},
        "locked": false,
        "visible": true,
        "attributes": []
    }, {
        "type": "polygon",
        "classId": 2,
        "probability": 100,
        "points": [493.7, 355.6, 506.1, 366.1, 567.3, 384.2, 648.5, 352.7, 649.5, 372.8, 659, 386.1, 649.5, 402.4, 631.3, 424.4, 618.9, 451.1, 612.2, 456.9, 585.4, 474.1, 570.1, 477.9, 552, 466.4, 527.1, 454, 491.8, 402.4, 494.6, 377.5, 493.7, 355.6],
        "groupId": 0,
        "pointLabels": {},
        "locked": false,
        "visible": true,
        "attributes": []
    }, {
        "type": "meta",
        "name": "lastAction",
        "timestamp": 1600628979793
    }]
}
classes.json
[{
    "attribute_groups": [],
    "color": "#32a852",
    "id": 1,
    "name": "no-mask",
    "opened": true
}, {
    "attribute_groups": [],
    "color": "#d6d619",
    "id": 2,
    "name": "mask",
    "opened": true
}]
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