I am using a U-Net architecture with Xception net as encoder . The visual area of the segmentation mask is very small and after training, it is giving a lot of false positives. I am thinking of changing the kernel size of 3 by 3 to 5 by 5. What precautions/care shall I take while creating the model? I am getting the issue of dimensionality mismatch if just change 3 by 3 to 5 by 5 kernel size.
Also,while concatenating the skip connections, the dimensions don't match
Hello!
I think the issue regarding dimensional mismatch can be fixed by modulating the stride and padding. You can look into the initial layers of AlexNet architecture (provided below) for better understanding.
model=Sequential()
model.add(Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), padding="valid", activation="relu", input_shape=(224,224,3)))
model.add(MaxPooling2D(pool_size=(3,3),strides=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(filters=256,kernel_size=(5,5),strides=(1,1), padding="valid", activation="relu"))
model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2)))
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