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.