<p>According to the <a href="https://arxiv.org/pdf/1512.03385v1.pdf" rel="nofollow noreferrer">paper</a> provided in Keras documentation you should provide a <code>224 x 224 RGB [0 - 225]</code> image. The actual dimension ordering depends on the backend you use in your Keras installation. </p>

<p>The data preparation was performed as in <a href="https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf" rel="nofollow noreferrer">AlexNet</a> so the mean activation was subtracted from each color channel. The mean vector for RGB is <code>103.939, 116.779, 123.68</code>.</p>

<p>If your color values would extend <code>-255, 255</code> range - it could harm your training because of the magnitude of data unknown for the network. But still - network could adapt to this changes, but it usually makes more time and make training more chaotic.</p>

<p>In case of monochromatic images - a commonly used technique is a repeating the same channel 3 times in order to make dimensions plausible for network architecture.</p>
What Keras's Resnet expects as an input?
<p>I'm trying to use Keras's implementation of resnet for a transfer learaning task with a quite different set of images (B&amp;W 16 bit). So what Keras expects as an input? Image with 3 channels and -127-128 range (that's what I assume zero centered 8 bit image)? 0-255? What would happen if I pass something outside this range?</p>

<p>Thanks.</p>