Fig. 1.
Schematic overview of the general architecture of a convolutional neural network. The input image is mapped into various feature maps via convolutions, followed by a pooling layer that performs local averaging and sub-sampling. The final layer is a list of outputs that, in our case, correspond to whether a bar is present or not.
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