CreateML JSON Annotation Format

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CreateML JSON

CreateML JSON

When Apple released CreateML in 2018 it was a groundbreaking innovation. For the first time, developers could easily train a machine learning model with no code. No code, that is, except for the one-off scripts they needed to write to munge their data into the proper JSON format that was adopted from Apple's acquired machine learning framework, Turi Create.

Fortunately, Roboflow can both read and write CreateML JSON files so you can truly train a model and export it for use with an Apple iOS device via CoreML without writing a single line of code. Follow our CreateML tutorial to learn how to use it to train a no-code object detection model.

CreateML JSON

Use Roboflow to convert

CreateML JSON

to the following formats.

Tensorflow TFRecord

YOLO Darknet TXT

YOLOv7 PyTorch TXT

YOLOv5 PyTorch TXT

Sagemaker GroundTruth Manifest

Google Cloud AutoML Vision CSV

Scaled-YOLOv4 TXT

OpenAI CLIP Classification

RetinaNet Keras CSV

Tensorflow Object Detection CSV

Multiclass Classification CSV

YOLOv4 PyTorch TXT

YOLOv5 Oriented Bounding Boxes

Use Roboflow to convert the following formats to

CreateML JSON

format.

YOLO Darknet TXT

Sagemaker GroundTruth Manifest

Google Cloud AutoML Vision CSV

SuperAnnotate JSON

Supervisely JSON

VGG Image Annotator JSON

VGG Image Annotator CSV

LabelBox Video JSON

IBM Cloud Annotations JSON

Unity Perception JSON

RetinaNet Keras CSV

Tensorflow Object Detection CSV

Multiclass Classification CSV

Kaggle Wheat CSV

Here are pre-configured models that use

CreateML 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.

createml.json

[
    {
        "image": "0001.jpg",
        "annotations": [
            {
                "label": "helmet",
                "coordinates": {
                    "x": 162.5,
                    "y": 45,
                    "width": 79,
                    "height": 88
                }
            },
            {
                "label": "person",
                "coordinates": {
                    "x": 145.5,
                    "y": 176,
                    "width": 251,
                    "height": 350
                }
            }
        ]
    }
]