Summary
In this chapter, we covered a variety of image data augmentation techniques. We learned how to implement an SVM with data augmentation in Python using the scikit-learn and Keras libraries. We first implemented SVM with the default hyperparameters and evaluated the performance of the classifier on the original dataset. We then implemented an SVM with data augmentation and trained the classifier on each batch of training data generated by the ImageDataGenerator object. Finally, we evaluated the performance of the classifier on the augmented dataset.
We also saw how to implement a CNN using augmentation with the CIFAR-10 dataset. Using data augmentation, we were able to improve the accuracy of the classifier on the augmented dataset. This demonstrates the effectiveness of data augmentation in improving the performance of machine learning models, especially in cases where the available dataset is limited.
Data augmentation can reduce the need for manual annotation by creating...
