DATASET: 'inn_stl10'  # choices = ['stl10', 'cifar10', 'cub20', 'bmw10', 'opets'] Add 'inn_' or 'kway_' for INN and B-ML specific dataset loaders
NUM_CLASSES: 10
IMG_SIZE: 96   # Input size for the network
BATCH_SIZE: 32 
RE_SIZE: 96   # Image is resized to this value before applying crop etc.
PRETRAINED: False  # If the model needs to be pretrained on Imgnet.
NUM_BINS: !!python/tuple [256]    #Not to be changed. 
TRANSFORMS: !!python/tuple ['CROP', 'HFLIP', 'ROTATION', 'NORM'] #Training pre-prcoessing Crop -> Flip -> rotate -> norm. Other values are 'RRCROP', 'RCROP'
LEARNING_RATES:
  '256': !!python/tuple [0.1, 350, [150, 200, 250, 300]] # Learning rate, Max epochs, LR steps
OPTIMIZER: 'SGD'   # Optimiser to use. Only supports SGD
ARCH: 'reduced_inn18'  # The models to use = {({'inn18', resnet18}, [cub20, bmw10, pets]), ({'reduced_inn18', 'reduced_resnet18'}, [cifar10, stl10]), ({'inn50', 'resnet50'}, [cub200])}
LOAD_WEIGHTS_FROM:    #Use it for iterative fine-tuning else leave it empty
pos_neg_balanced: False  #Disabled else provide a list of weights = [1.0, 2.0]. Works only for INN
num_negatives: 9    # Value of N to set
class_in_activation: 'none'  # Activation to use for label encoder
neg_red_factor:  #Disabled by default. Applies random noise to input 1-hot encoding
weight:    #Don't forget to switch when not training inn model
  'inn': 1
  'cls': 0
