Ggtcf

I have a question about the use of the sample_weight parameter in the context of data augmentation in Keras with the ImageDataGenerator. Let's say I have a series of simple images with just one class of objects. So, for each image, I will have a corresponding mask with pixels = 0 for the background and 1 for where the object is labeled.

However, this dataset is unbalanced because a significant amount of these images are empty, which mean with masks just containing 0.
If I understood well, the 'sample_weight' parameter of the flow method of ImageDataGenerator is here to put the focus on the the samples of my dataset that I find more interesting, i.e. where my object is present.

My question is: what is the concrete influence of this sample_weight parameter on the training of my model. Does it influence the data augmentation? If I use the 'validation_split' parameter, does it influence the way validation sets are generated?

Here is the part of my code my question refers to:

data_gen_args = dict(rotation_range=90,
 width_shift_range=0.4,
 height_shift_range=0.4,
 zoom_range=0.4,
 horizontal_flip=True,
 fill_mode='reflect',
 rescale=1. / 255,
 validation_split=0.2,
 data_format='channels_last'
) 

image_datagen = ImageDataGenerator(**data_gen_args)


imf = image_datagen.flow(
 x=stacked_images_channel,
 y=stacked_masks_channel,
 batch_size=batch_size,
 shuffle=False,
 seed=seed,subset='training',
 sample_weight = sample_weight,
 save_to_dir = 'traindir',
 save_prefix = 'train_'
)

valf = image_datagen.flow(
 x=stacked_images_channel,
 y=stacked_masks_channel,
 batch_size=batch_size,
 shuffle=False,
 seed=seed,subset='validation',
 sample_weight = sample_weight,
 save_to_dir = 'valdir',
 save_prefix = 'val_'
)

STEP_SIZE_TRAIN=imf.n//imf.batch_size
STEP_SIZE_VALID=valf.n//valf.batch_size

model = unet.UNet2(numberOfClasses, imshape, '', learningRate, depth=4)

history = model.fit_generator(generator=imf,
 steps_per_epoch=STEP_SIZE_TRAIN,
 epochs=epochs,
 validation_data=valf,
 validation_steps=STEP_SIZE_VALID,
 verbose=2
)

Thank you in advance for your attention.