| classification-on-indl | VGG16 | #2 | Average Recall: 92.86% |
| face-anti-spoofing-on-celeba-spoof-enroll5 | VGG16 | #3 | AUC: 98.0 |
| face-anti-spoofing-on-siw-enroll5 | VGG16 | #5 | AUC: 97.8 |
| image-to-image-translation-on-gtav-to-cityscapes-labels | VGG16 60.3 | #24 | mIoU: 41.3 |
| image-classification-on-imagenet-real | VGG-16 BN | #56 | Accuracy: 80.60% |
| image-classification-on-imagenet-real | VGG-16 | #57 | Accuracy: 79.01% |
| domain-generalization-on-vizwiz-classification | VGG-16 BN | #57 | Accuracy - All Images: 36.7Accuracy - Corrupted Images: 31.1… |
| domain-generalization-on-vizwiz-classification | VGG-19 BN | #62 | Accuracy - All Images: 36.2Accuracy - Corrupted Images: 29.4… |
| domain-generalization-on-vizwiz-classification | VGG-19 | #73 | Accuracy - All Images: 34.7Accuracy - Corrupted Images: 29… |
| domain-generalization-on-vizwiz-classification | VGG-16 | #74 | Accuracy - All Images: 34.7Accuracy - Corrupted Images: 28.5… |
| domain-generalization-on-vizwiz-classification | VGG-13 BN | #78 | Accuracy - All Images: 33.7Accuracy - Corrupted Images: 28.3… |
| domain-generalization-on-vizwiz-classification | VGG-11 BN | #80 | Accuracy - All Images: 32.9Accuracy - Corrupted Images: 25.8… |
| domain-generalization-on-vizwiz-classification | VGG-13 | #82 | Accuracy - All Images: 32.4Accuracy - Corrupted Images: 26.4… |
| domain-generalization-on-vizwiz-classification | VGG-11 | #83 | Accuracy - All Images: 31.5Accuracy - Corrupted Images: 25.2… |
| image-classification-on-imagenet | VGG (Net-D + Net-E) | #957 | Top 1 Accuracy: 76.3% |