Performance degradation of ImageNet trained models by simple image transformations
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ImageNet trained PyTorch models are generally preferred as the off-the-shelf models for direct use or for initialisation in most computer vision tasks. In this paper, we simply test a representative set of these convolution and transformer based models under many simple image transformations like horizontal shifting, vertical shifting, scaling, rotation, presence of Gaussian noise, cutout, horizontal flip and vertical flip and report the performance drop caused by such transformations. We find that even simple transformations like rotating the image by 10 or zooming in by 20 accuracy of models like ResNet152 by 1 https://github.com/harshm121/imagenet-transformation-degradation.
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