machine learning - Can Keras deal with input images with different size?

Question

Can the Keras deal with input images with different size? For example, in the fully convolutional neural network, the input images can have any size. However, we need to specify the input shape when we create a network by Keras. Therefore, how can we use Keras to deal with different input size without resizing the input images to the same size? Thanks for any help.

Answer 1

Yes. Just change your input shape to shape=(n_channels, None, None). Where n_channels is the number of channels in your input image.

I'm using Theano backend though, so if you are using tensorflow you might have to change it to (None,None,n_channels)

You should use:

input_shape=(1, None, None)

None in a shape denotes a variable dimension. Note that not all layers will work with such variable dimensions, since some layers require shape information (such as Flatten). https://github.com/fchollet/keras/issues/1920

For example, using keras's functional API your input layer would be:

For a RGB dataset

inp = Input(shape=(3,None,None))

For a Gray dataset

inp = Input(shape=(1,None,None))