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Models/Pre trained wide-resnet-101

Pre trained wide-resnet-101

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

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

Computer Vision8 results

Image ClassificationonCaltech-101
Accuracy· 2021-03-21
97.76
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonFruits-360
Accuracy· 2021-03-21
99.97
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonSTL-10
Accuracy· 2021-03-21
98.18
best: 99.7 (TURTLE (CLIP + DINOv2))
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonBird-225
Accuracy· 2021-03-21
99.55
best: 99.56 (WideResNet-101 (Spinal FC))
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonCaltech-101
Accuracy· 2021-03-21
97.76
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonFruits-360
Accuracy· 2021-03-21
99.97
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonSTL-10
Accuracy· 2021-03-21
98.18
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonBird-225
Accuracy· 2021-03-21
99.55
best: 99.56 (WideResNet-101 (Spinal FC))
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373