My CNN is an extremely simple neural network.

input_img = Input(shape=(80, 80, 1))

x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

decoder = Model(input_img, decoded)
decoder.compile(optimizer='adadelta', loss='binary_crossentropy')

As you can see, it is a fully convolutional neural network. Once it get trained, can I use this CNN as a kernel for convolution operated on a large, high-res image, meaning can I do something like this?

output = scipy.signal.convolve(target image (4k x 4k), CNN (trained on 80 x 80 pix), mode='same')

I need this to do image segmentation. I know there are U-nets to do segmenttion, but my problem doesn't require such a big network, and I need something extremely light and fast.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.