I’m new to Image Processing/ Classification so I’m sorry if this is common knowledge. How do I decide which of ImageDataGenerator parameters/ features I should implement on my dataset? There are quite a few parameters in ImageDataGenerator,

tf.keras.preprocessing.image.ImageDataGenerator(
    featurewise_center=False,
    samplewise_center=False,
    featurewise_std_normalization=False,
    samplewise_std_normalization=False,
    zca_whitening=False,
    zca_epsilon=1e-06,
    rotation_range=0,
    width_shift_range=0.0,
    height_shift_range=0.0,
    brightness_range=None,
    shear_range=0.0,
    zoom_range=0.0,
    channel_shift_range=0.0,
    fill_mode='nearest',
    cval=0.0,
    horizontal_flip=False,
    vertical_flip=False,
    rescale=None,
    preprocessing_function=None,
    data_format=None,
    validation_split=0.0,
    interpolation_order=1,
    dtype=None
)

I know a few things like,

    rescale=1.0/255,                # Rescale pixel values to [0, 1]
    rotation_range=20,              # Random rotation within 20 degrees
    width_shift_range=0.2,          # Random horizontal shift by 20% of image width
    height_shift_range=0.2,         # Random vertical shift by 20% of image height
    horizontal_flip=True,           # Random horizontal flipping
    fill_mode='nearest'             # Fill mode for new pixels after shifts/rotations

I’m not sure about the rest of the parameters. I understand that every dataset is different and needs to be processed according to it’s features and the task at hand. But, I would like to understand what I’m doing/ what else could be done.

Thank you.

I’m currently working on a Dogs & Cats Images dataset and I want to classify the images using tensorflow. Implementing ImageDataGenerator has been confusing.