How do I implement custom data augmentation pipelines to improve model training

Question

With the help of proper code, can you tell me how I implement custom data augmentation pipelines to improve model training?

Answer 1

To implement custom data augmentation pipelines, you can use libraries like TensorFlow or PyTorch to apply transformations such as cropping, rotation, flipping, or noise addition to your training data.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Custom Augmentation: Apply random transformations (flip, rotate, brightness, etc.) using TensorFlow's tf.image functions.
• Pipeline: Use tf.data.Dataset to map and apply augmentations to images in the dataset.
• Training: Feed augmented data to the model for training.

Hence, this custom pipeline allows you to adapt augmentations to your specific needs, improving model generalization and robustness.