![]() If you use this software, please cite it using the metadata from this CITATION. Keep safe_triplet default False value but be careful with choosing the batch_size so you do not end up with a last batch containing a single class (or a single sample)īatch size is calculated by multiplying num_classes_per_batch and num_images_per_class.If you are to use this generator with TripletLoss, your should either: This however guarantee that both num_classes_per_batch and num_images_per_class are fixed for all batches including later ones. But as sampling weighted per class, every epoch will include a very high percentage of the dataset and should approach 100% as dataset size increases. trainaug ImageDataGenerator ( rescale1./255, horizontalflipTrue, heightshiftrange0.1, widthshiftrange0.1, brightnessrange (0.5,1.5), zoomrange 1, 1.5, ) trainaugds trainaug.flowfromdirectory ( directory'. It is a part of the Keras deep learning framework, which provides a high-level API for building and training deep learning models. Setting safe_triplet to True does not guarantee that every epoch will include all different samples from the dataset. Keras2 ImageDataGenerator is a data generator that produces batches of augmented image data. Setting safe_triplet to False (Default) makes sure that every image is seen exactly one time per epoch but it does not guarantee a fixed num_classes_per_batch or num_images_per_class in later batches. If there is no enough samples remaining from the choosen class, it is skipped and another class is choosen (This behaviour can be disabled and we indefinitely repeat the datasets) ![]() Your directory structure should be: main_directory/īehind the scenes, this API creates a different dataset for every class and by using weighted random sampling, a number of classes is drawn (num_classes_per_batch) and a specific number of images is selected from every choosen class (num_images_per_class) as long as there are enough samples from this class. Generates a balanced per batch tf.data.Dataset from image files in a directory. In python, next() applied to a generator yields one sample from the generator. 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 ) The datagenerator object is a python generator and yields (x,y) pairs on every step. This datagenerator is compatible with TripletLoss as it guarantees the existence of postive pairs in every batch. A shoutout to Jason Brownlee who provides a on this. A Keras/Tensorflow compatible image data generator for creating balanced batches. Augmenting our image data with keras is dead simple.
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