https://github.com/apache/incubator-mxnet/blob/a38278ddebfcc9459d64237086cd7977ec20c70e/example/image-classification/train_imagenet.py#L42

When I try to train imagenet with this line commented, the train-accuracy reaches 99% while the validation-accuracy is only less than 50% (single machine, 8 GPUs, global batchsize=2048, Resnet50). Absolutely this is overfitting.

Then I uncomment this line and try again with the same experiment settings. This time both train and validation accuracy converge to about 70%. 

Thus, it seems that this data augmentation is pretty important for ImageNet training. Perhaps it will be better to uncomment this as default, so that future developers won't get confused by the over-fit issue.