| medical-image-classification-on-nct-crc-he-100k | ResNeXt-50-32x4d | #2 | Accuracy (%): 95.46F1-Score: 97.46Precision: 99.91… |
| classification-on-indl | ResNetV2_50 | #8 | Average Recall: 88.08% |
| domain-generalization-on-vizwiz-classification | ResNet-50 (gn) | #9 | Accuracy - All Images: 48.9Accuracy - Corrupted Images: 39.1… |
| image-classification-on-inaturalist-2019 | ResNet50 (A2) | #13 | Top-1 Accuracy: 75.0 |
| fine-grained-image-classification-on-oxford-102-flowers | ResNet50 (A1) | #16 | Accuracy: 97.9%FLOPS: 4.1PARAMS: 24M |
| image-classification-on-flowers-102 | ResNet50 (A1) | #38 | Accuracy: 97.9FLOPS: 4.1PARAMS: 25M |
| image-classification-on-imagenet-real | ResNet50 (A1) | #45 | Accuracy: 85.7%Params: 25M |
| image-classification-on-imagenet-v2 | ResNet50 (A1) | #46 | Top 1 Accuracy: 68.7 |
| fine-grained-image-classification-on-stanford-cars | ResNet50 (A1) | #55 | Accuracy: 92.7%FLOPS: 4.1BPARAMS: 24M |
| image-classification-on-cifar-10 | ResNet50 (A1) | #61 | Percentage correct: 98.3 |
| image-classification-on-cifar-10 | cvpr_class | #249 | Percentage correct: 85.28 |
| image-classification-on-cifar-100 | ResNet50 (A1) | #73 | Percentage correct: 86.9PARAMS: 25M |
| image-classification-on-imagenet | ResNet-152 (A2 + reg) | #573 | Top 1 Accuracy: 82.4%Number of params: 60.2M |
| image-classification-on-imagenet | ResNet-152 (A2) | #642 | Top 1 Accuracy: 81.8%Number of params: 60.2M |
| image-classification-on-imagenet | DeiT-S (T2) | #743 | Top 1 Accuracy: 80.4%Number of params: 22M |
| image-classification-on-imagenet | ResNet50 (A1) | #744 | Top 1 Accuracy: 80.4%Number of params: 25M |
| image-classification-on-imagenet | ResNet50 (A3) | #888 | Top 1 Accuracy: 78.1%Number of params: 25M |