Note, though, that the preprocessing and augmentation is (at least in TF) done within the framework itself. I helped debug the pure-TensorFlow version of the I

Note, though, that the preprocessing and augmentation is (at least in TF) done within the framework itself. I helped debug the pure-TensorFlow version of the Inception input pipeline, and getting it to match the earlier DistBelief version was agonizing -- it really shows all of the differences (and bugs) in the image processing ops. And there can be subtle effects -- differences in which image resizing algorithm you use, for example. But it's worth noting that this code is all released: https://github.com/tensorflow/models/blob/master/inception/inception/image_processing.py#L198 https://github.com/tensorflow/models/blob/master/inception/i... It may be hard to replicate that across all platforms, though -- as an example, the distortions include using four different image resizing algorithms. Some of it was true preprocessing, i.e., cleaning up the imagenet data. I wrote a bit about that here: https://da-data.blogspot.com/2016/02/cleaning-imagenet-dataset-collected.html https://da-data.blogspot.com/2016/02/cleaning-imagenet-datas... (tl;dr - there are some invalid images and bboxes, etc., and some papers chose to deal with the "blacklisted" images differently.)