  Data-augmentation is key to the training of neural networks for image
classification. This paper first shows that existing augmentations induce a
significant discrepancy between the typical size of the objects seen by the
classifier at train and test time. We experimentally validate that, for a
target test resolution, using a lower train resolution offers better
classification at test time.
  We then propose a simple yet effective and efficient strategy to optimize the
classifier performance when the train and test resolutions differ. It involves
only a computationally cheap fine-tuning of the network at the test resolution.
This enables training strong classifiers using small training images. For
instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained
on 128x128 images, and 79.8% with one trained on 224x224 image. In addition, if
we use extra training data we get 82.5% with the ResNet-50 train with 224x224
images.
  Conversely, when training a ResNeXt-101 32x48d pre-trained in
weakly-supervised fashion on 940 million public images at resolution 224x224
and further optimizing for test resolution 320x320, we obtain a test top-1
accuracy of 86.4% (top-5: 98.0%) (single-crop). To the best of our knowledge
this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date.
