| data-augmentation-on-imagenet | ResNet-200 (AA) | #6 | Accuracy (%): 80.0 |
| data-augmentation-on-imagenet | ResNet-50 (AA) | #12 | Accuracy (%): 77.6 |
| domain-generalization-on-vizwiz | EfficientNet-B3 (autoaug) | #22 | Accuracy - All Images: 42.6Accuracy - Corrupted Images: 34.9… |
| domain-generalization-on-vizwiz | EfficientNet-B2 (autoaug) | #28 | Accuracy - All Images: 41.6Accuracy - Corrupted Images: 34.3… |
| domain-generalization-on-vizwiz | EfficientNet-B1 (autoaug) | #38 | Accuracy - All Images: 39.7Accuracy - Corrupted Images: 32.8… |
| domain-generalization-on-vizwiz | EfficientNet-B0 (autoaug) | #72 | Accuracy - All Images: 34.9Accuracy - Corrupted Images: 27.3… |
| fine-grained-image-classification-on-caltech | AutoAugment | #12 | Top-1 Error Rate: 13.07% |
| fine-grained-image-classification-on-fgvc | AutoAugment | #26 | Accuracy: 92.67%Top-1 Error Rate: 7.33 |
| fine-grained-image-classification-on-oxford | AutoAugment | #20 | Accuracy: 95.36%Top-1 Error Rate: 4.64% |
| fine-grained-image-classification-on-oxford-1 | AutoAugment | #10 | Accuracy: 88.98%Top-1 Error Rate: 11.02% |
| fine-grained-image-classification-on-stanford | AutoAugment | #21 | Accuracy: 94.8% |
| image-classification-on-cifar-100 | PyramidNet+ShakeDrop | #32 | Percentage correct: 89.3 |