Formats
LabelBox JSON
Object Detection
IMPORTEXPORT
FEATURED
Object Detection

LabelBox JSON

LabelBox is an outsourced label management service, specializing in on-premise "bring your own labeler" solutions.

Overview

LabelBox hosts a free online "label it yourself" tool and is a popular solution for outsourced labeling. Their key differentiator is that you can bring your own labeling team. This is important for domains where labeling needs to be done by a specially trained expert (for example, labeling crop diseases or medical images).

You can export from LabelBox in two formats, CSV or JSON. No computer vision models will accept these formats directly. But fear not, Roboflow supports importing LabelBox JSON annotations and can convert them to any format your machine learning model requires.

Format Description

Below, learn the structure of LabelBox JSON.

labelbox.json
[{
    "ID": "a9b7c5d3e1f",
    "DataRow ID": "xy10z8a6b4c",
    "Labeled Data": "https://storage.labelbox.com/IMG_001.JPG",
    "Label": {
        "helmet": [{
            "geometry": [{
                "x": 690,
                "y": 1497
            }, {
                "x": 931,
                "y": 1497
            }, {
                "x": 931,
                "y": 1633
            }, {
                "x": 690,
                "y": 1633
            }]
        }, {
            "geometry": [{
                "x": 1079,
                "y": 1445
            }, {
                "x": 1324,
                "y": 1445
            }, {
                "x": 1324,
                "y": 1564
            }, {
                "x": 1079,
                "y": 1564
            }]
        }]
    },
    "Project Name": "Boggle",
    "External ID": "IMG_001.JPG"
}]

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

Split detections in LabelBox JSON

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Merge detections in LabelBox JSON

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Supported Models

Below, see model architectures that require data in the LabelBox 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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Convert Data from LabelBox JSON

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Formats
LabelBox JSON

What is LabelBox JSON?

LabelBox hosts a free online "label it yourself" tool and is a popular solution for outsourced labeling. Their key differentiator is that you can bring your own labeling team. This is important for domains where labeling needs to be done by a specially trained expert (for example, labeling crop diseases or medical images).

You can export from LabelBox in two formats, CSV or JSON. No computer vision models will accept these formats directly. But fear not, Roboflow supports importing LabelBox JSON annotations and can convert them to any format your machine learning model requires.

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LabelBox JSON

Below, we show how to convert data to and from

LabelBox JSON

. We also list popular models that use the

LabelBox JSON

data format. Our conversion tools are free to use.

CONVERT To
CONVERT From
EXAMPLE

Convert Data to LabelBox JSON

Use Roboflow to convert
LabelBox 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

LabelBox JSON
 to 
YOLOv8 PyTorch TXT
LabelBox JSON
 to 
YOLOv8 Oriented Bounding Boxes
LabelBox JSON
 to 
PaliGemma JSONL
LabelBox JSON
 to 
YOLOv10 PyTorch TXT
LabelBox JSON
 to 
YOLOv9 PyTorch TXT
LabelBox JSON
 to 
Florence-2
LabelBox JSON
 to 
YOLOv11 PyTorch TXT
LabelBox JSON
 to 
OpenAI GPT-4o Object Detection JSONL
LabelBox JSON
 to 
COCO Run-Length Encoding (RLE)
LabelBox JSON
 to 
Tensorflow TFRecord
LabelBox JSON
 to 
COCO JSON
LabelBox JSON
 to 
Pascal VOC XML
LabelBox JSON
 to 
YOLO Darknet TXT
LabelBox JSON
 to 
CreateML JSON
LabelBox JSON
 to 
YOLOv7 PyTorch TXT
LabelBox JSON
 to 
YOLOv5 PyTorch TXT
LabelBox JSON
 to 
Sagemaker GroundTruth Manifest
LabelBox JSON
 to 
Google Cloud AutoML Vision CSV
LabelBox JSON
 to 
meituan/yolov6
LabelBox JSON
 to 
Scaled-YOLOv4 TXT
LabelBox JSON
 to 
YOLO Keras TXT
LabelBox JSON
 to 
OpenAI CLIP Classification
LabelBox JSON
 to 
RetinaNet Keras CSV
LabelBox JSON
 to 
Tensorflow Object Detection CSV
LabelBox JSON
 to 
Multiclass Classification CSV
LabelBox JSON
 to 
YOLOv4 PyTorch TXT
LabelBox JSON
 to 
YOLOv5 Oriented Bounding Boxes

Convert Data from LabelBox JSON

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

What computer vision models use LabelBox JSON?

The

No items found.

models all use the

LabelBox 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.
labelbox.json
[{
    "ID": "a9b7c5d3e1f",
    "DataRow ID": "xy10z8a6b4c",
    "Labeled Data": "https://storage.labelbox.com/IMG_001.JPG",
    "Label": {
        "helmet": [{
            "geometry": [{
                "x": 690,
                "y": 1497
            }, {
                "x": 931,
                "y": 1497
            }, {
                "x": 931,
                "y": 1633
            }, {
                "x": 690,
                "y": 1633
            }]
        }, {
            "geometry": [{
                "x": 1079,
                "y": 1445
            }, {
                "x": 1324,
                "y": 1445
            }, {
                "x": 1324,
                "y": 1564
            }, {
                "x": 1079,
                "y": 1564
            }]
        }]
    },
    "Project Name": "Boggle",
    "External ID": "IMG_001.JPG"
}]
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