Keras Image Classification for Different Input Sizes

Question

I want to use Tensorflow and Keras to train a dataset composed of images with very different sizes in order to classify them. But some of them are horizontal (1400x100) and some of them are vertical (100x1000). As far as I understand, Keras accepts same size input images. I'm not sure if it's wise to convert all of them to a classical resolution like 150x150 or 180x180 since they are horizontal and vertical.

How can I solve this problem?

Answer 1

A few methods have been developed to process images with multiple sizes, including images with unequal horizontal and vertical dimensions. For example, spatial pyramid pooling or scale recurrent neural networks . You could also set network dimensions to be variable , then use a pooling operation (eg global average pooling) to get fixed size dimensions before fully connected or other layers than need a fixed size.

The simplest approach is cropping or padding images (eg with zeros) so that they are all the same size.