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ImageNet trained PyTorch models are generally preferred as the off-the-shelfmodels for direct use or for initialisation in most computer vision tasks. Inthis paper, we simply test a representative set of these convolution andtransformer based models under many simple image transformations likehorizontal shifting, vertical shifting, scaling, rotation, presence of Gaussiannoise, cutout, horizontal flip and vertical flip and report the performancedrop caused by such transformations. We find that even simple transformationslike rotating the image by 10{\deg} or zooming in by 20% can reduce the top-1accuracy of models like ResNet152 by 1%+. The code is available athttps://github.com/harshm121/imagenet-transformation-degradation.

Performance degradation of ImageNet trained models by simple image transformations