| fine-grained-image-classification-on-birdsnap | FixSENet-154 | #3 | Accuracy: 84.3% |
| fine-grained-image-classification-on-oxford-iiit-pet | FixSENet-154 | #9 | Accuracy: 94.8%Top-1 Error Rate: 5.2% |
| image-classification-on-inaturalist | FixSENet-154 | #11 | Top 1 Accuracy: 75.4 |
| fine-grained-image-classification-on-nabirds | FixSENet-154 | #14 | Accuracy: 89.2% |
| fine-grained-image-classification-on-cub-200-2011 | FixSENet-154 | #17 | Accuracy: 88.7 |
| fine-grained-image-classification-on-oxford-102-flowers | FixInceptionResNet-V2 | #19 | Accuracy: 95.7%Top-1 Error Rate: 4.3% |
| image-classification-on-imagenet-real | FixResNeXt-101 32x48d | #27 | Accuracy: 89.73%Params: 829M |
| fine-grained-image-classification-on-stanford-cars | FixSENet-154 | #32 | Accuracy: 94.4% |
| image-classification-on-imagenet | FixResNeXt-101 32x48d | #154 | Top 1 Accuracy: 86.4%Number of params: 829MHardware Burden: 62G… |
| image-classification-on-imagenet | FixResNet-50 Billion-scale@224 | #560 | Top 1 Accuracy: 82.5%Number of params: 25.6M |
| image-classification-on-imagenet | FixResNet-50 CutMix | #774 | Top 1 Accuracy: 79.8% |
| image-classification-on-imagenet | FixResNet-50 | #813 | Top 1 Accuracy: 79.1% |