Creating training and test datasets
A dataset is a set of images and labels that describe those images. Your project needs a training dataset and a test dataset. Amazon Rekognition Custom Labels uses the training dataset to train your model. After training, Amazon Rekognition Custom Labels uses the test dataset to verify how well the trained model predicts the correct labels.
You can create datasets with the Amazon Rekognition Custom Labels console or with the AWS SDK. Before creating a dataset, we recommend reading Understanding Amazon Rekognition Custom Labels. For other dataset tasks, see Managing datasets.
The steps creating training and tests datasets for a project are:
To create training and test datasets for your project
Determine how you need to label your training and test datasets. For more information, Purposing datasets.
Collect the images for your training and test datasets. For more information, see Preparing images.
Create the training and test datasets. For more information, see Creating training and test datasets with images. If you're using the AWS SDK, see Create training and test datasets (SDK) .
If necesessary, add image-level labels or bounding boxes to your dataset images. For more information, see Labeling images.
After you create the datasets, you can train the model.
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