Fixing the train-test resolution discrepancy

Benchmark Model Rank Results
fine-grained-image-classification-on-birdsnapFixSENet-154#3Accuracy: 84.3%
fine-grained-image-classification-on-cub-200-1FixSENet-154#15Accuracy: 88.7
fine-grained-image-classification-on-nabirdsFixSENet-154#14Accuracy: 89.2%
fine-grained-image-classification-on-oxfordFixInceptionResNet-V2#19Accuracy: 95.7%Top-1 Error Rate: 4.3%
fine-grained-image-classification-on-oxford-1FixSENet-154#9Accuracy: 94.8%Top-1 Error Rate: 5.2%
fine-grained-image-classification-on-stanfordFixSENet-154#32Accuracy: 94.4%
image-classification-on-imagenetFixResNeXt-101 32x48d#140Top 1 Accuracy: 86.4%Number of params: 829MHardware Burden: 62G
image-classification-on-imagenetFixResNet-50 Billion-scale@224#510Top 1 Accuracy: 82.5%Number of params: 25.6M
image-classification-on-imagenetFixResNet-50 CutMix#697Top 1 Accuracy: 79.8%
image-classification-on-imagenetFixResNet-50#733Top 1 Accuracy: 79.1%
image-classification-on-imagenet-realFixResNeXt-101 32x48d#23Accuracy: 89.73%Params: 829M
image-classification-on-inaturalistFixSENet-154#11Top 1 Accuracy: 75.4