With this approach, you create your training data by uploading and annotating images manually. You create your test data by either uploading and annotating test images or by auto-splitting to have Rekognition automatically use a portion of your training data as test images.
Uploading and annotating
images
To train the adapter, you’ll need to upload a set of sample images representative of your use case. For best results, provide as many images for training as possible up to the limit of 10000, and ensure the images are representative of all aspects of your use-case.
When using the AWS Console you can upload images directly from your computer, provide a manifest file, or provide an Amazon S3 bucket that stores your images.
However, when using the Rekognition APIs with an SDK, you must provide a manifest file that references images stored in an Amazon S3 bucket.
You can use the Rekognition
console
Create a test set
You will need to provide an annotated test set or use the auto-split feature. The training set is used to actually train the adapter. The adapter learns the patterns contained in these annotated images. The test set is used to evaluate the model's performance before finalizing the adapter.
Train the adapter
Once you have finished annotating the training data, or have provided a manifest file, you can initiate the training process for your adapter.
Get the Adapter ID
Once the adapter has been trained, you can get the unique ID for your adapter to use with Rekognition's image analysis APIs.
Call the API operation
To apply your custom adapter, provide its ID when calling one of the image analysis APIs that supports adapters. This enhances the accuracy of predictions for your images.
