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Models/Res2Net-50

Res2Net-50

Reported on 11 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.

Methodology4 results

ClassificationonNCT-CRC-HE-100K
Accuracy (%)· 2019-04-02
93.37
best: 95.59 (Efficientnet-b0)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
ClassificationonNCT-CRC-HE-100K
F1-Score· 2019-04-02
96.25
best: 97.48 (Efficientnet-b0)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
ClassificationonNCT-CRC-HE-100K
Precision· 2019-04-02
99.93
best: 100 (ResNet-50)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
ClassificationonNCT-CRC-HE-100K
Specificity· 2019-04-02
99.17
best: 99.45 (Efficientnet-b0)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169

Medical4 results

Medical Image ClassificationonNCT-CRC-HE-100K
Accuracy (%)· 2019-04-02
93.37
best: 95.59 (Efficientnet-b0)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
Medical Image ClassificationonNCT-CRC-HE-100K
F1-Score· 2019-04-02
96.25
best: 97.48 (Efficientnet-b0)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
Medical Image ClassificationonNCT-CRC-HE-100K
Precision· 2019-04-02
99.93
best: 100 (ResNet-50)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
Medical Image ClassificationonNCT-CRC-HE-100K
Specificity· 2019-04-02
99.17
best: 99.45 (Efficientnet-b0)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169

Computer Vision3 results

Image ClassificationonGasHisSDB
Accuracy· 2019-04-02
98.68
best: 98.74 (CoAtNet-1)
SOTA
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
Image ClassificationonGasHisSDB
F1-Score· 2019-04-02
99.29
best: 99.38 (CoAtNet-1)
SOTA
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169
Image ClassificationonGasHisSDB
Precision· 2019-04-02
99.91
best: 99.97 (CoAtNet-1)
Res2Net: A New Multi-scale Backbone Architecture arXiv:1904.01169