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Models/RegNetY-3.2GF

RegNetY-3.2GF

Reported on 11 benchmarks across 3 tasks · 1 paper · 1 SOTA

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

Methodology4 results

ClassificationonNCT-CRC-HE-100K
Accuracy (%)· 2021-01-03
95.42
best: 95.59 (Efficientnet-b0)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
ClassificationonNCT-CRC-HE-100K
F1-Score· 2021-01-03
97.39
best: 97.48 (Efficientnet-b0)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
ClassificationonNCT-CRC-HE-100K
Precision· 2021-01-03
99.97
best: 100 (ResNet-50)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
ClassificationonNCT-CRC-HE-100K
Specificity· 2021-01-03
99.43
best: 99.45 (Efficientnet-b0)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590

Medical4 results

Medical Image ClassificationonNCT-CRC-HE-100K
Accuracy (%)· 2021-01-03
95.42
best: 95.59 (Efficientnet-b0)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
Medical Image ClassificationonNCT-CRC-HE-100K
F1-Score· 2021-01-03
97.39
best: 97.48 (Efficientnet-b0)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
Medical Image ClassificationonNCT-CRC-HE-100K
Precision· 2021-01-03
99.97
best: 100 (ResNet-50)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
Medical Image ClassificationonNCT-CRC-HE-100K
Specificity· 2021-01-03
99.43
best: 99.45 (Efficientnet-b0)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590

Computer Vision3 results

Image ClassificationonGasHisSDB
Precision· 2021-01-03
99.97
SOTA
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
Image ClassificationonGasHisSDB
Accuracy· 2021-01-03
97.48
best: 98.74 (CoAtNet-1)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590
Image ClassificationonGasHisSDB
F1-Score· 2021-01-03
98.7
best: 99.38 (CoAtNet-1)
RegNet: Self-Regulated Network for Image Classification arXiv:2101.00590