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Models/GauNet (ResNet-50)

GauNet (ResNet-50)

Reported on 9 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.

Computer Vision9 results

CrowdsonShanghaiTech B
MAE· uses extra data· 2022-06-10
6
best: 5.51 (EBC-ZIP-B)
SOTA
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
CrowdsonUCF-QNRF
MAE· 2022-06-10
81.6
best: 69.46 (EBC-ZIP-B)
SOTA
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
CrowdsonJHU-CROWD++
MAE· 2022-06-10
58.2
best: 54.3 (APGCC)
SOTA
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
CrowdsonJHU-CROWD++
MSE· 2022-06-10
245.1
best: 225.9 (APGCC)
SOTA
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
Object CountingonTRANCOS
MAE· 2022-06-10
2.1
SOTA
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
Object CountingonTRANCOS
MSE· 2022-06-10
2.6
SOTA
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
CrowdsonShanghaiTech A
MAE· 2022-06-10
54.8
best: 47.81 (EBC-ZIP-B)
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
CrowdsonShanghaiTech A
MSE· 2022-06-10
89.1
best: 76.7 (APGCC)
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253
CrowdsonUCF CC 50
MAE· 2022-06-10
186.3
best: 154.8 (APGCC)
Rethinking Spatial Invariance of Convolutional Networks for Object Counting arXiv:2206.05253