Q: Image centering in ResNet-50 in tensorflow-slim I am using ResNet-50 model in tensorflow-slim to extract features. My question is do I need to centre the images according to some standard ResNets-50 mean values before feeding in the images? I know that for vgg-19 tf-slim provides options for centering using 
_mean_image_subtraction(image, means) defined in vgg_preprocessing.py . But I couldn't find any such files or functions for ResNets.

A: I believe you should use vgg_preprocessing as well. In get_preprocessing() from preprocessing_factory.py:
preprocessing_fn_map = {
  'cifarnet': cifarnet_preprocessing,
  'inception': inception_preprocessing,
  'inception_v1': inception_preprocessing,
  'inception_v2': inception_preprocessing,
  'inception_v3': inception_preprocessing,
  'inception_v4': inception_preprocessing,
  'inception_resnet_v2': inception_preprocessing,
  'lenet': lenet_preprocessing,
  'mobilenet_v1': inception_preprocessing,
  'resnet_v1_50': vgg_preprocessing,
  'resnet_v1_101': vgg_preprocessing,
  'resnet_v1_152': vgg_preprocessing,
  'resnet_v1_200': vgg_preprocessing,
  'resnet_v2_50': vgg_preprocessing,
  'resnet_v2_101': vgg_preprocessing,
  'resnet_v2_152': vgg_preprocessing,
  'resnet_v2_200': vgg_preprocessing,
  'vgg': vgg_preprocessing,
  'vgg_a': vgg_preprocessing,
  'vgg_16': vgg_preprocessing,
  'vgg_19': vgg_preprocessing,
}

You can also verify the use of preprocessing_factory.get_preprocessing() from train_image_classifier.py:
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
    preprocessing_name,
    is_training=True)

...

image = image_preprocessing_fn(image, train_image_size, train_image_size)


A: Yes, you should center (normalize) images. It should be done for better model convergence.
If there is no default tf-slim method for for this image processing operation for ResNet, you may make your own implementation based on this variant
Also it is more useful to count means (from _mean_image_subtraction(image, means) ) for the whole training batch, not for the single image or for the whole dataset.
