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Models/ResNeXt-101 32x16d

ResNeXt-101 32x16d

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

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· uses extra data· 2016-11-16
51.7
best: 57.2 (VOLO-D5)
SOTA
Aggregated Residual Transformations for Deep Neural Networks arXiv:1611.05431
Domain AdaptationonVizWiz-Classification
Accuracy - Clean Images· uses extra data· 2016-11-16
54.8
best: 450 (ViT-8/B-224)
SOTA
Aggregated Residual Transformations for Deep Neural Networks arXiv:1611.05431
Domain AdaptationonVizWiz-Classification
Accuracy - Corrupted Images· uses extra data· 2016-11-16
48.1
best: 51.8 (VOLO-D5)
SOTA
Aggregated Residual Transformations for Deep Neural Networks arXiv:1611.05431

Computer Vision3 results

Domain GeneralizationonVizWiz-Classification
Accuracy - All Images· uses extra data· 2016-11-16
51.7
best: 57.2 (VOLO-D5)
SOTA
Aggregated Residual Transformations for Deep Neural Networks arXiv:1611.05431
Domain GeneralizationonVizWiz-Classification
Accuracy - Clean Images· uses extra data· 2016-11-16
54.8
best: 450 (ViT-8/B-224)
SOTA
Aggregated Residual Transformations for Deep Neural Networks arXiv:1611.05431
Domain GeneralizationonVizWiz-Classification
Accuracy - Corrupted Images· uses extra data· 2016-11-16
48.1
best: 51.8 (VOLO-D5)
SOTA
Aggregated Residual Transformations for Deep Neural Networks arXiv:1611.05431