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Models/VOLO-D5+HAT

VOLO-D5+HAT

Reported on 7 benchmarks across 3 tasks · 1 paper · 2 SOTA

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

Computer Vision4 results

Domain GeneralizationonStylized-ImageNet
Top 1 Accuracy· 2022-04-03
25.9
best: 32.77 (MAE+DAT (ViT-H))
SOTA
Improving Vision Transformers by Revisiting High-frequency Components arXiv:2204.00993
Image ClassificationonImageNet
GFLOPs· 2022-04-03
412
best: 1478 (InternImage-H)
Improving Vision Transformers by Revisiting High-frequency Components arXiv:2204.00993
Domain GeneralizationonImageNet-R
Top-1 Error Rate· 2022-04-03
40.3
best: 3.9 (Model soups (BASIC-L))
Improving Vision Transformers by Revisiting High-frequency Components arXiv:2204.00993
Domain GeneralizationonImageNet-C
mean Corruption Error (mCE)· 2022-04-03
38.4
best: 28.2 (DINOv2 (ViT-g/14, frozen model, linear eval))
Improving Vision Transformers by Revisiting High-frequency Components arXiv:2204.00993

Methodology3 results

Domain AdaptationonStylized-ImageNet
Top 1 Accuracy· 2022-04-03
25.9
best: 32.77 (MAE+DAT (ViT-H))
SOTA
Improving Vision Transformers by Revisiting High-frequency Components arXiv:2204.00993
Domain AdaptationonImageNet-R
Top-1 Error Rate· 2022-04-03
40.3
best: 3.9 (Model soups (BASIC-L))
Improving Vision Transformers by Revisiting High-frequency Components arXiv:2204.00993
Domain AdaptationonImageNet-C
mean Corruption Error (mCE)· 2022-04-03
38.4
best: 22 (EfficientNet-L2+RPL)
Improving Vision Transformers by Revisiting High-frequency Components arXiv:2204.00993