kerasgen

A Keras/Tensorflow compatible image data generator for creating balanced batches. This datagenerator is compatible with TripletLoss as it guarantees the existence of postive pairs in every batch.

Installation

pip install kerasgen

Usage

from kerasgen.balanced_image_dataset import balanced_image_dataset_from_directory

train_ds = balanced_image_dataset_from_directory(
    directory, num_classes_per_batch=2,
    num_images_per_class=5, image_size=(256, 256),
    validation_split=0.2, subset='training', seed=555,
    safe_triplet=True)

val_ds = balanced_image_dataset_from_directory(
    directory, num_classes_per_batch=2,
    num_images_per_class=5, image_size=(256, 256),
    validation_split=0.2, subset='validation', seed=555,
    safe_triplet=True)

Generates a balanced per batch tf.data.Dataset from image files in a directory.

Your directory structure should be:

main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg

Setting safe_triplet to True does not guarantee that every epoch will include all different samples from the dataset. But as sampling is weighted per class, every epoch will include a very high percentage of the dataset and should approach 100% as dataset size increases. This however guarantee that both num_classes_per_batch and num_images_per_class are fixed for all batches including later ones.

Batch size is calculated by multiplying num_classes_per_batch and num_images_per_class.

Requirements

• Tensorflow >= 2.7
• Numpy >= 1.19

Compatible with

• tf.data.Dataset API
• TripletHardLoss
• TripletSemiHardLoss

Licence

MIT

Authors

kerasgen was written by Mahmoud Bahaa <mah.alaa@nu.edu.eg>.