# Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning
## 3 MobileNetV2
\begin{table}
\begin{tabular}{l l l l l} \hline
**Head:Layers Name** & **Output Size** & **Parameters Used** & \\ \hline Input-I (input layer) & (224,224,3) & 0 & & \\ Convolution1D/Convolution2D & (112,112,32) & 864 & & \\ Batch Normalization\_Conv1 & (112,112,32) & 128 & & \\ ReLU\_Conv1 & (112,112,32) & 0 & & \\ Extended Depthwise\_Conv & (112,112,32) & 288 & & \\ Extended Depthwise\_Conv\_BN & (112,112,32) & 128 & & \\ Extended Depthwise\_Conv\_ReLU & (112,112,32) & 0 & & \\ Extended Convolution2D & (112,112,16) & 512 & & \\ Extended Batch Normalization\_Conv & (112,112,16) & 64 & & \\ \hline Block-1:Layers Name & Output size & Parameters used & Block-2:Layers Name & Output size & Parameters used \\ \hline Extended\_Conv2D & (112,112,96) & 1536 & Extended\_Conv2D & (56,56,144) & 3456 \\ Extended\_BN & (112,112,96) & 384 & Extended\_BN & (56,56,144) & 576 \\ Extended\_ReLU & (112,112,96) & 0 & Extended\_ReLU & (56,56,144) & 0 \\ Zero Padding2D & (113,113,96) & 0 & Depthwise\_Convolution & (56,56,144) & 1296 \\ Depthwise\_Convolution & (56,56,96) & 864 & Depthwise\_BN & (56,56,144) & 576 \\ Depthwise\_BN & (56,56,96) & 384 & Depthwise\_ReLU & (56,56,144) & 0 \\ Depthwise\_ReLU & (56,56,96) & 0 & Convolution2D & (56,56,24) & 3456 \\ Convolution2D & (56,56,24) & 2304 & Batch Normalization & (56,56,24) & 96 \\ Batch Normalization & (56,56,24) & 96 & Add & \\ \hline Block-3:Layers Name & Output size & Parameters used & Block-4:Layers Name & Output size & Parameters used \\ \hline Extended\_Conv2D & (56,56,144) & 3456 & Extended\_Conv2D & (28,28,192) & 6144 \\ Extended\_BN & (56,56,144) & 576 & Extended\_BN & (28,28,192) & 768 \\ Extended\_ReLU & (56,56,144) & 0 & Extended\_Relu & (28,28,192) & 0 \\ Zero Padding2D & (57,57,144) & 0 & Depthwise\_Conv & (28,28,192) & 1728 \\ Depthwise\_Conv & (28,28,144) & 1296 & Depthwise\_BN & (28,28,192) & 768 \\ Depthwise\_BN & (28,28,144) & 576 & Depthwise\_ReLU & (28,28,192) & 0 \\ Depthwise\_ReLU & (28,28,144) & 0 & Convolution2D & (28,28,32) & 6144 \\ Convolution2D & (28,28,32) & 4608 & Batch Normalization & (28,28,32) & 128 \\ Batch Normalization & (28,28,32) & 128 & Add & (28,28,32) & 0 \\ \hline Block-5:Layers Name & Output size & Parameters used & Block-6:Layers Name & Output size & Parameters used \\ \hline Extended\_Conv2D & (28,28,192) & 6144 & Extended\_Conv2D & (28,28,192) & 6144 \\ Extended\_BN & (28,28,192) & 768 & Extended\_BN & (28,28,192) & 768 \\ Extended\_ReLU & (28,28,192) & 0 & Extended\_ReLU & (28,28,192) & 0 \\ Depthwise\_Conv & (28,28,192) & 1728 & Zero Padding2D & (29,29,192) & 0 \\ Depthwise\_BN & (28,28,192) & 768 & Depthwise\_Conv & (14,14,192) & 1728 \\ Depthwise\_ReLU & (28,28,192) & 0 & Depthwise\_BN & (14,14,192) & 768 \\ Convolution2D & (28,28,32) & 6144 & Depthwise\_ReLU & (14,14,192) & 0 \\ Batch Normalization & (28,28,32) & 128 & Convolution2D & (14,14,64) & 12,288 \\ Add & (28,28,32) & 0 & Batch Normalization & (14,14,64) & 256 \\ \hline Block-7:Layers Name & Output size & Parameters used & Block-8:Layers Name & Output size & Parameters used \\ \hline Extended\_Conv2D & (14,14,384) & 24,576 & Extended\_Conv2D & (14,14,384) & 24,576 \\ Extended\_BN & (14,14,384) & 1536 & Extended\_BN & (14,14,384) & 1536 \\ Extended\_ReLU & (14,14,384) & 0 & Extended\_ReLU & (14,14,384) & 0 \\ Depthwise\_Conv & (14,14,384) & 3456 & Depthwise\_Conv & (14,14,384) & 3456 \\ Depthwise\_BN & (14,14,384) & 1536 & Depthwise\_BN & (14,14,384) & 1536 \\ Depthwise\_ReLU & (14,14,384) & 0 & Depthwise\_ReLU & (14,14,384) & 0 \\ Convolution2D & (14,14,64) & 24,576 & Convolution2D & (14,14,64) & 24,576 \\ Batch Normalization & (14,14,64) & 256 & Batch Normalization & (14,14,64) & 256 \\ Add & (14,14,64) & 0 & Add & (14,14,64) & 0 \\ \hline Block-9:Layers Name & Output size & Parameters used & Block-10:Layers Name & Output size & Parameters used \\ \hline Extended\_Conv2D & (14,14,384) & 24,576 & Extended\_Conv2D & (14,14,384) & 24,576 \\ Extended\_BN & (14,14,384) & 1536 & Extended\_BN & (14,14,384) & 1536 \\ Extended\_ReLU & (14,14,384) & 0 & Extended\_ReLU & (14,14,384) & 0 \\ Depthwise\_Conv & (14,14,384) & 3456 & Depthwise\_Conv & (14,14,384) & 3456 \\ Depthwise\_BN & (14,14,384) & 1536 & Depthwise\_BN & (14,14,384) & 1536 \\ Depthwise\_ReLU & (14,14,384) & 0 & Depthwise\_ReLU & (14,14,384) & 0 \\ Convolution2D & (14,14,64) & 24,576 & Convolution2D & (14,14,64) & 24,576 \\ Batch Normalization & (14,14,64) & 256 & Batch Normalization & (14,14,64) & 256 \\ Add & (14,14,64) & 0 & Add & (14,14,64) & 0 \\ \hline Block-9:Layers Name & Output size & Parameters used & Block-10:Layers Name & Output size & Parameters used \\ \hline Extended\_Conv2D & (14,14,384) & 24,576 & Extended\_Conv2D & (14,14,384) & 24,576 \\ Extended\_BN & (14,14,384) & 1536 & Extended\_BN & (14,14,384) & 1536 \\ Extended\_ReLU & (14,14,384) & 0 & Extended\_ReLU & (14,14,384) & 0 \\ Depthwise\_Conv & (14,14,384) & 3456 & Depthwise\_Conv & (14,14,384) & 3456 \\ Depthwise\_BN & (14,14,384) & 1536 & Depthwise\_BN & (14,14,384) & 1536 \\ Depthwise\_ReLU & (14,14,384) & 0 & Depthwise\_ReLU & (14,14,384) & 0 \\ Convolution2D & (14,14,64) & 24,576 & Convolution2D & (14,14,96) & 36,864 \\ Batch Normalization & (14,14,64) & 256 & Batch Normalization & (14,14,96) & 384 \\ Add & (14,14,64) & 0 & & \\ \hline Block-10:Layers Name & Output size & Parameters used & Block-10:Layers Name & Output size & Parameters used \\ \hline Extended\_Conv2D & (14,14,384) & 24,576 & Extended\_Conv2D & (14,14,384) & 24,576 \\ Extended\_BN & (14,14,384) & 1536 & Extended\_BN & (14,14,384) & 1536 \\ Extended\_ReLU & (14,14,384) & 0 & Extended\_ReLU & (14,14,384) & 0 \\ Depthwise\_Conv & (14,14,384) & 3456 & Depthwise\_Conv & (14,The five additional layers used in the pretrained architecture of the MobileNetV2 model are highlighted in bold.