vgg16 architecture
model = Sequential() model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding="same", activation="relu")) model.add(Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu")) model.add(MaxPool2D(pool_size=(2,2),strides=(2,2))) model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu")) model.add(MaxPool2D(pool_size=(2,2),strides=(2,2))) model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu")) model.add(MaxPool2D(pool_size=(2,2),strides=(2,2))) model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu")) model.add(MaxPool2D(pool_size=(2,2),strides=(2,2))) model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu")) model.add(Conv2D(filters=512, kernel_size=(3,3), padding="same", activation="relu")) model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
