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Models/VGG-5

VGG-5

Reported on 10 benchmarks across 2 tasks · 2 papers · 7 SOTA

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

Computer Vision11 results

Image ClassificationonEMNIST-Digits
Accuracy· 2021-03-21
99.82
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonQMNIST
Accuracy· 2021-03-21
99.6867
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonEMNIST-Letters
Accuracy· 2021-03-21
95.86
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonKuzushiji-MNIST
Accuracy· 2021-03-21
98.98
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonEMNIST-Digits
Accuracy· 2021-03-21
99.82
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Fine-Grained Image ClassificationonQMNIST
Accuracy· 2021-03-21
99.6867
SOTA
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonEMNIST-Balanced
Trainable Parameters· 2020-07-07
3646000
SOTA
SpinalNet: Deep Neural Network with Gradual Input arXiv:2007.03347
Image ClassificationonEMNIST-Letters
Accuracy· 2021-03-21
95.86
best: 95.96 (WaveMixLite-112/16)
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonEMNIST-Letters
Accuracy· 2021-03-21
95.86
best: 95.96 (WaveMixLite-112/16)
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonKuzushiji-MNIST
Accuracy· 2021-03-21
98.98
best: 99.35 (KMNIST-Tiny)
ProgressiveSpinalNet architecture for FC layers arXiv:2103.11373
Image ClassificationonEMNIST-Balanced
Accuracy· 2020-07-07
91.04
best: 91.48 (EMNIST-mobile)
SpinalNet: Deep Neural Network with Gradual Input arXiv:2007.03347