Amazon SageMaker GroundTruth is a popular option for outsourced labeling jobs. They use AI to assist their human annotators in creating high quality data for training computer vision models.
Unfortunately, the format is poorly documented and is not widely used outside the Amazon ecosystem (to learn about Amazon's one-click training tool, see our Rekognition Custom Labels tutorial). If you want to get your images labeled with GroundTruth and use them with your own models you'll need to convert them to a standard format first. That's where Roboflow comes in; our Pro tier can parse manifest files and convert them into any other format.
Since AWS credits can be used for GroundTruth labeling jobs, this can be the most cost-effective way for startups to get their images labeled! Roboflow's Pro tier can even handle the API integration to manage the whole process end to end.
As an added bonus, if you've already labeled your data elsewhere, you can use Roboflow Pro to convert your existing annotations into a manifest file for use with Amazon Rekognition Custom Labels' one-click Object Detection AutoML service.
Below, learn the structure of Sagemaker GroundTruth Manifest.
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{"source-ref":"s3://roboflow/img010.jpg","roboflow":{"annotations":[{"class_id":1,"width":64,"top":4,"height":84,"left":179},{"class_id":1,"width":36,"top":85,"height":37,"left":40},{"class_id":1,"width":46,"top":64,"height":44,"left":69},{"class_id":1,"width":27,"top":110,"height":34,"left":2}],"image_size":[{"width":480,"depth":3,"height":318}]},"roboflow-metadata":{"job-name":"labeling-job/roboflow","class-With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in Sagemaker GroundTruth Manifest. Read our dedicated guides to learn how to merge and split Sagemaker GroundTruth Manifest detections.
Below, see model architectures that require data in the Sagemaker GroundTruth Manifest 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.