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Models/ResNet-50 (SIN)

ResNet-50 (SIN)

Reported on 6 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology3 results

Domain AdaptationonVizWiz-Classification
Accuracy - All Images· 2018-11-29
25.3
best: 57.2 (VOLO-D5)
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness arXiv:1811.12231
Domain AdaptationonVizWiz-Classification
Accuracy - Clean Images· 2018-11-29
30
best: 450 (ViT-8/B-224)
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness arXiv:1811.12231
Domain AdaptationonVizWiz-Classification
Accuracy - Corrupted Images· 2018-11-29
20.4
best: 51.8 (VOLO-D5)
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness arXiv:1811.12231

Computer Vision3 results

Domain GeneralizationonVizWiz-Classification
Accuracy - All Images· 2018-11-29
25.3
best: 57.2 (VOLO-D5)
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness arXiv:1811.12231
Domain GeneralizationonVizWiz-Classification
Accuracy - Clean Images· 2018-11-29
30
best: 450 (ViT-8/B-224)
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness arXiv:1811.12231
Domain GeneralizationonVizWiz-Classification
Accuracy - Corrupted Images· 2018-11-29
20.4
best: 51.8 (VOLO-D5)
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness arXiv:1811.12231