How To Convert Sagemaker GroundTruth Manifest to CreateML JSON

Formats



Sagemaker GroundTruth Manifest



Convert to

CreateML JSON

Sagemaker GroundTruth Manifest

The proprietary annotation format used by Amazon SageMaker GroundTruth.





CreateML JSON

Apple's CreateML and Turi Create tools need a special JSON format for object detection tasks.

How To Convert Sagemaker GroundTruth Manifest to CreateML JSON

If your labeling tool exported annotations in the

GroundTruth

format, but you’re trying to use a

model that needs

CreateML

annotations, we’ve got you covered. You can convert those in 3 clicks with Roboflow.

Step 1: Create a Free Roboflow Public Workspace

Roboflow is the universal conversion tool for computer vision annotation formats. The Public plan is the best way for those exploring personal projects, class assignments, and other experiments to try Roboflow. To convert your dataset, start by creating a workspace on the Public plan.

Create Roboflow Workspace

Step 2: Upload your data into Roboflow

Once your account has been created, click Create Dataset.

Upload your data to Roboflow by dragging and dropping your

Sagemaker GroundTruth Manifest

images and annotations into the upload space.

To learn more about Amazon's related hosted cloud training service, see our full breakdown of Amazon Rekognition Custom Labels.

Step 3: Generate Dataset Version

Next, you can choose Preprocessing and Augmentation options for your dataset version and then click Generate.

Step 4: Export Dataset Version

After generating, you will be prompted to Export your dataset. You can choose to receive your dataset as a .zip file or a curl download link. You can also choose which format you would like to export. Choose

CreateML JSON

Congratulations, you have successfully converted your dataset from

Sagemaker GroundTruth Manifest

format to

CreateML JSON

format!

Next Steps

Ready to use your new

CreateML

dataset?

Training a no-code object detection model with CreateML is easy, just follow our CreateML tutorial.

Here are some compatible models:

Try an end-to-end computer vision tutorial, check out your dataset health check or experiment with some augmentations.