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Formats
IBM Cloud Annotations JSON
Convert to
Sagemaker GroundTruth Manifest
IBM Cloud Annotations JSON
The native JSON format of IBM's open source Cloud Annotations labeling tool.
Sagemaker GroundTruth Manifest
The proprietary annotation format used by Amazon SageMaker GroundTruth.

How To Convert IBM Cloud Annotations JSON to Sagemaker GroundTruth Manifest

If your labeling tool exported annotations in the

Cloud Annotations

format, but you’re trying to use a

model that needs

GroundTruth

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
IBM Cloud Annotations JSON
images and annotations into the upload space.
Upload Data To Roboflow

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

Sagemaker GroundTruth Manifest

.

Congratulations, you have successfully converted your dataset from

IBM Cloud Annotations JSON

format to

Sagemaker GroundTruth Manifest

format!

Next Steps

Ready to use your new

GroundTruth

dataset?

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

Here are some compatible models:

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