|
Blocks | Layers | Values |
|
Conv_bloc 1 | Convolution2D BatchNormalization2D Activation Function | Number of filters: 64 Kernel size: (3,3) |
Convolution2D | - ReLu Number of filters: 128 Kernel size: (3,3) |
|
Conv_bloc 2 | BatchNormalization2D Activation Function MaxPooling2D Convolution2D | - ReLu Kernel size: (2,2) with stride: 2 Number of filters: 128 Kernel size: (3,3) |
|
Res_1 | BatchNormalization2D Activation Function Convolution2D | - ReLu Number of filters: 128 Kernel size: (3,3) |
BatchNormalization2D Activation Function Convolution2D | - ReLu Number of filters: 256 Kernel size: (3,3) |
|
Conv_bloc 3 | BatchNormalization2D Activation Function MaxPooling2D Convolution2D | - ReLu Kernel size: (2,2) with stride: 2 Number of filters: 512 Kernel size: (3,3) |
|
Conv_bloc 4 | BatchNormalization2D Activation Function MaxPooling2D Convolution2D | - ReLu Kernel size: (2,2) with stride: 2 Number of filters: 512 Kernel size: (3,3) |
|
Res_2 | BatchNormalization2D Activation Function Convolution2D | - ReLu Number of filters: 512 Kernel size: (3,3) |
|
MaxPool | BatchNormalization2D Activation Function AdaptiveMaxPooling2D Flatten | - ReLu - - |
|
Bloc_classification | Dropout Dense Activation Function | 0.2 512 Linear |
|