config.py

import time


class Config:
    labels = ['pre1935', '1935-1955', '1956-1971', '1972-1990', 'post1990']

    num_classes = len(labels)

    image_size = 224

    input_shape = [image_size, image_size, 3]

    lr_max = 0.0001  # maximum learning rate

    lr_min = 0.000001  # minimum learning rate, note you can set lr_min = lr_max to use constant learning rate

    optimizer = 'adam'  # 'adam' or 'sgd'

    early_stop_patience = 5

    transfer_learning = 1  # 1 if transfer learning is used, 0 otherwise

    batch_size = 64 # higher batch size is faster but requires more memory

    num_epochs = 100 # max number of epochs 

    loss = 'crossentropy'

    val = 1  # 1 if validation input is available, 0 otherwise

    saved_model_dir = "weights/20241206-225836"  # leave empty to defaul model

    in_dir = "input/"

    timestr = str(time.strftime("%Y%m%d-%H%M%S"))

    out_dir = "output/" + str(timestr) + "/"