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Models/RetinaNet (ResNet-50)

RetinaNet (ResNet-50)

Reported on 10 benchmarks across 5 tasks · 1 paper

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

Methodology8 results

3DonCOCO-O
Average mAP· 2017-08-07
16.6
best: 57.8 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
3DonCOCO-O
Effective Robustness· 2017-08-07
0.18
best: 28.86 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
2D ClassificationonCOCO-O
Average mAP· 2017-08-07
16.6
best: 57.8 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
2D ClassificationonCOCO-O
Effective Robustness· 2017-08-07
0.18
best: 28.86 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
2D Object DetectiononCOCO-O
Average mAP· 2017-08-07
16.6
best: 57.8 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
2D Object DetectiononCOCO-O
Effective Robustness· 2017-08-07
0.18
best: 28.86 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
16konCOCO-O
Average mAP· 2017-08-07
16.6
best: 57.8 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
16konCOCO-O
Effective Robustness· 2017-08-07
0.18
best: 28.86 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002

Computer Vision2 results

Object DetectiononCOCO-O
Average mAP· 2017-08-07
16.6
best: 57.8 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002
Object DetectiononCOCO-O
Effective Robustness· 2017-08-07
0.18
best: 28.86 (EVA)
Focal Loss for Dense Object Detection arXiv:1708.02002