| fine-grained-image-classification-on-birdsnap | EffNet-L2 (SAM) | #1 | Accuracy: 90.07% |
| fine-grained-image-classification-on-oxford-iiit-pets | EffNet-L2 (SAM) | #1 | Accuracy: 97.10Top-1 Error Rate: 2.90% |
| image-classification-on-cifar-100 | EffNet-L2 (SAM) | #1 | Percentage correct: 96.08 |
| image-classification-on-cifar-100 | PyramidNet (SAM) | #45 | Percentage correct: 89.7 |
| image-classification-on-cifar-100 | CNN39 | #235 | Percentage correct: 42.64 |
| image-classification-on-cifar-100 | CNN36 | #236 | Percentage correct: 36.07 |
| fine-grained-image-classification-on-food-101 | EffNet-L2 (SAM) | #2 | Accuracy: 96.18 |
| fine-grained-image-classification-on-stanford-cars | EffNet-L2 (SAM) | #7 | Accuracy: 95.96% |
| image-classification-on-flowers-102 | EffNet-L2 (SAM) | #8 | Accuracy: 99.65% |
| image-classification-on-imagenet | EfficientNet-L2-475 (SAM) | #39 | Top 1 Accuracy: 88.61%Number of params: 480M |
| image-classification-on-imagenet | ResNet-152 (SAM) | #656 | Top 1 Accuracy: 81.6% |
| fine-grained-image-classification-on-fgvc-aircraft | EffNet-L2 (SAM) | #46 | Top-1 Error Rate: 4.82 |