How to Use Test-Time Augmentation to Make Better Predictions

Last Updated on April 3, 2020

Data augmentation is a technique often used to improve performance and reduce generalization error when training neural network models for computer vision problems.

The image data augmentation technique can also be applied when making predictions with a fit model in order to allow the model to make predictions for multiple different versions of each image in the test dataset. The predictions on the augmented images can be averaged, which can result in better predictive performance.

In this tutorial, you will discover test-time augmentation for improving the performance of models for image classification tasks.

After completing this tutorial, you will know:

• Test-time augmentation is the application of data augmentation techniques normally used during training when making predictions.
• How to implement test-time augmentation from scratch in Keras.
• How to use test-time augmentation to improve the performance of a convolutional neural network model on a standard image classification task.

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