| object-detection-on-pascal-voc-2007 | ResNet-101 (baseline+++: +box refinement, +context, +multi-scale testing) | #3 | MAP: 85.6% |
| image-classification-on-gashissdb | ResNet-50 | #4 | Accuracy: 98.56Precision: 99.94F1-Score: 99.24 |
| image-classification-on-gashissdb | ResNet-18 | #5 | Accuracy: 98.47Precision: 99.94F1-Score: 99.19 |
| medical-image-classification-on-nct-crc-he-100k | ResNet-50 | #4 | Accuracy (%): 94.72F1-Score: 97.09Precision: 100.00… |
| medical-image-classification-on-nct-crc-he-100k | ResNet-18 | #7 | Accuracy (%): 92.66F1-Score: 95.23Precision: 99.90… |
| synthetic-to-real-translation-on-syn2real-c | No Adaptation | #5 | Accuracy: 52.4 |
| breast-tumour-classification-on-pcam | ResNet-50 (e) | #6 | AUC: 0.948 |
| breast-tumour-classification-on-pcam | ResNet-34 (e) | #7 | AUC: 0.942 |
| retinal-oct-disease-classification-on-oct2017 | ResNet50-v1 | #6 | Acc: 99.3Sensitivity: 99.3 |
| person-re-identification-on-sysu-30k | ResNet-50 (generalization) | #8 | Rank-1: 20.1 |
| image-classification-on-omnibenchmark | ResNet-101 | #10 | Average Top-1 Accuracy: 37.4 |
| image-classification-on-omnibenchmark | ResNet-50 | #15 | Average Top-1 Accuracy: 34.3 |
| retinal-oct-disease-classification-on-srinivasan2014 | ResNet50-v1 | #10 | Acc: 94.92 |
| domain-generalization-on-vizwiz-classification | ResNet-152 | #11 | Accuracy - All Images: 47.5Accuracy - Corrupted Images: 43.3… |
| domain-generalization-on-vizwiz-classification | ResNet-101 | #12 | Accuracy - All Images: 46.3Accuracy - Corrupted Images: 40.5… |
| domain-generalization-on-vizwiz-classification | ResNet-50 | #20 | Accuracy - All Images: 42.9Accuracy - Corrupted Images: 37.1… |
| unsupervised-domain-adaptation-on-office-home | ResNet-50 [cite:CVPR16DRL] | #15 | Accuracy: 59.9 |
| crowd-counting-on-ucf-qnrf | Resnet101 | #16 | MAE: 190 |
| semantic-segmentation-on-dada-seg | ResNet-101 | #16 | mIoU: 23.60 |
| semantic-segmentation-on-dada-seg | ResNet-50 | #24 | mIoU: 18.96 |
| image-to-image-translation-on-gtav-to-cityscapes-labels | ResNet101 65.1 | #23 | mIoU: 41.7 |
| domain-generalization-on-imagenet-r | ResNet-50 | #35 | Top-1 Error Rate: 63.9 |
| domain-generalization-on-imagenet-a | ResNet-50 (300 Epochs) | #36 | Top-1 accuracy %: 4.2 |
| domain-adaptation-on-office-31 | ResNet-50 | #37 | Average Accuracy: 76.1 |
| semantic-segmentation-on-cityscapes-val | Dilated-ResNet (Dilated-ResNet-101) | #67 | mIoU: 75.7 |
| object-detection-on-coco-test-dev | ResNet-101 (ensemble of 3 models) | #188 | box mAP: 37.4 |
| image-classification-on-cifar-10 | ResNet-110 | #201 | Percentage correct: 93.57 |
| image-classification-on-imagenet | ResNet-152 | #863 | Top 1 Accuracy: 78.57%GFLOPs: 11.3 |
| image-classification-on-imagenet | ResNet-101 | #881 | Top 1 Accuracy: 78.25%Number of params: 40MGFLOPs: 7.6 |
| image-classification-on-imagenet | ResNet-50 | #993 | Top 1 Accuracy: 75.3%Number of params: 25MGFLOPs: 3.8 |