mrtpk

# ref: https://arxiv.org/pdf/1602.07360.pdf
# ref: https://medium.com/@smallfishbigsea/notes-of-squeezenet-4137d51feef4
# ref: https://github.com/cmasch/squeezenet/blob/master/squeezenet.py
# ref: https://towardsdatascience.com/review-squeezenet-image-classification-e7414825581a

# import dependencies
from keras.models import Model
import keras.layers as layer

def get_fire_module(input_tensor, name, s1x1, use_bypass=False):

mrtpk

# Network inspired from VGG and UNET
# ref: https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47
# ref: https://tuatini.me/practical-image-segmentation-with-unet/
# ref: https://github.com/zhixuhao/unet/blob/master/model.py

# import dependencies
from keras.models import Model
import keras.layers as layer

def basic_block(input_tensor, bloc_name, n_convs, n_filters):

mrtpk

def vgg16(input_shape=(224, 224, 3), classes=1000):
    '''
    ref: https://arxiv.org/abs/1409.1556
    default input_shape is 224, 224, 3
    default number of class is 1000
    '''
    # import dependencies
    # from keras.models import Model
    # import keras.layers as layer