super-gradients/src/super_gradients/recipes/arch_params/efficientnet_arch_params.yaml

backbone_mode: False # cuts off classification head
batch_norm_momentum: 0.99 # value used for the running_mean and running_var computation
batch_norm_epsilon: 1e-3 # value added to the denominator for numerical stability
image_size: # net's input size

# see doc in super_gradients/training/models/efficientnet.py round_filters
width_coefficient:
depth_divisor: 8
min_depth:
depth_coefficient:

dropout_rate: # dropout probability in final layer
num_classes: # number of outputs of the classification head
drop_connect_rate: 0.2 # connection dropout probability