Formats
Scale AI JSON
Object Detection
IMPORTEXPORT
FEATURED
Object Detection

Scale AI JSON

Scale AI is a popular outsourced labeling platform; their API returns object detection annotations in this JSON format.

Overview

Once you're ready to get serious about a computer vision project, you are probably going to need to outsource your labeling. One popular service for this is Scale.com. Unfortunately, the results they send back are not in a format that is ready to go into common machine learning models.

That's where Roboflow comes in. Simply drop in the JSON files you receive from Scale's human annotators and we'll convert them to whatever format you need for your model.

Format Description

Below, learn the structure of Scale AI JSON.

scale.ai.json
[
  {
    "task_id": "a9b7c5d3e1f",
    "created_at": "2000-01-01T00:00:00.000Z",
    "completed_at": "2000-01-01T00:00:00.000Z",
    "callback_url": "example@example.com",
    "type": "annotation",
    "status": "completed",
    "instruction": "\n# Instructions\n\nLabel the helmets on the workers.",
    "params": {
      "attachment": "https://storage.googleapis.com/roboflow/0001.jpg",
      "attachment_type": "image",
      "objects_to_annotate": [
        "helmet",
        "head",
        "person"
      ],
      "with_labels": true,
      "min_width": 0,
      "min_height": 0,
      "examples": []
    },
    "is_test": false,
    "urgency": "standard",
    "metadata": {},
    "callback_succeeded": true,
    "processed_attachments": [],
    "project": "Hard Hat Workers",
    "response": {
      "annotations": [
        {
          "width": 189,
          "height": 144,
          "label": "helmet",
          "left": 1374,
          "top": 2128
        },
        {
          "width": 157,
          "height": 122,
          "label": "person",
          "left": 1427,
          "top": 2409
        }
      ]
    },
    "project_param_version": 0,
    "scale_internal_attachment": "https://d2qt2k6tuv6mdh.cloudfront.net/a9b7c5d3e1f/001.jpg",
    "workStarted": 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 Scale AI JSON. Read our dedicated guides to learn how to merge and split Scale AI JSON detections.

Split detections in Scale AI JSON

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Merge detections in Scale AI JSON

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Supported Models

Below, see model architectures that require data in the Scale AI 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 Scale AI JSON

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Formats
Scale AI JSON

What is Scale AI JSON?

Once you're ready to get serious about a computer vision project, you are probably going to need to outsource your labeling. One popular service for this is Scale.com. Unfortunately, the results they send back are not in a format that is ready to go into common machine learning models.

That's where Roboflow comes in. Simply drop in the JSON files you receive from Scale's human annotators and we'll convert them to whatever format you need for your model.

Deploy Models with Ease on Roboflow

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

Get Started Today

Scale AI JSON

Below, we show how to convert data to and from

Scale AI JSON

. We also list popular models that use the

Scale AI JSON

data format. Our conversion tools are free to use.

CONVERT To
CONVERT From
EXAMPLE

Convert Data to Scale AI JSON

Use Roboflow to convert
Scale AI 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

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

Convert Data from Scale AI JSON

Use Roboflow to convert the following formats to
Scale AI 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
Scale AI JSON
data format
.

What computer vision models use Scale AI JSON?

The

No items found.

models all use the

Scale AI 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.
scale.ai.json
[
  {
    "task_id": "a9b7c5d3e1f",
    "created_at": "2000-01-01T00:00:00.000Z",
    "completed_at": "2000-01-01T00:00:00.000Z",
    "callback_url": "example@example.com",
    "type": "annotation",
    "status": "completed",
    "instruction": "\n# Instructions\n\nLabel the helmets on the workers.",
    "params": {
      "attachment": "https://storage.googleapis.com/roboflow/0001.jpg",
      "attachment_type": "image",
      "objects_to_annotate": [
        "helmet",
        "head",
        "person"
      ],
      "with_labels": true,
      "min_width": 0,
      "min_height": 0,
      "examples": []
    },
    "is_test": false,
    "urgency": "standard",
    "metadata": {},
    "callback_succeeded": true,
    "processed_attachments": [],
    "project": "Hard Hat Workers",
    "response": {
      "annotations": [
        {
          "width": 189,
          "height": 144,
          "label": "helmet",
          "left": 1374,
          "top": 2128
        },
        {
          "width": 157,
          "height": 122,
          "label": "person",
          "left": 1427,
          "top": 2409
        }
      ]
    },
    "project_param_version": 0,
    "scale_internal_attachment": "https://d2qt2k6tuv6mdh.cloudfront.net/a9b7c5d3e1f/001.jpg",
    "workStarted": true
  }
]
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