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Probabilistic Anchor Assignment with IoU Prediction for Object Detection

@inproceedings{paa-eccv2020,
  title={Probabilistic Anchor Assignment with IoU Prediction for Object Detection},
  author={Kim, Kang and Lee, Hee Seok},
  booktitle = {ECCV},
  year={2020}
}

Results and Models

We provide config files to reproduce the object detection results in the ECCV 2020 paper for Probabilistic Anchor Assignment with IoU Prediction for Object Detection.

Backbone Lr schd Mem (GB) Score voting box AP Config Download
R-50-FPN 12e 3.7 True 40.4 config model | log
R-50-FPN 12e 3.7 False 40.2 -
R-50-FPN 18e 3.7 True 41.4 config model | log
R-50-FPN 18e 3.7 False 41.2 -
R-50-FPN 24e 3.7 True 41.6 config model | log
R-50-FPN 36e 3.7 True 43.3 config model | log
R-101-FPN 12e 6.2 True 42.6 config model | log
R-101-FPN 12e 6.2 False 42.4 -
R-101-FPN 24e 6.2 True 43.5 config model | log
R-101-FPN 36e 6.2 True 45.1 config model | log

Note:

  1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.2 mAP. We report the best results.