paa

PAA

Probabilistic Anchor Assignment with IoU Prediction for Object Detection

Abstract

In object detection, determining which anchors to assign as positive or negative samples, known as anchor assignment, has been revealed as a core procedure that can significantly affect a model's performance. In this paper we propose a novel anchor assignment strategy that adaptively separates anchors into positive and negative samples for a ground truth bounding box according to the model's learning status such that it is able to reason about the separation in a probabilistic manner. To do so we first calculate the scores of anchors conditioned on the model and fit a probability distribution to these scores. The model is then trained with anchors separated into positive and negative samples according to their probabilities. Moreover, we investigate the gap between the training and testing objectives and propose to predict the Intersection-over-Unions of detected boxes as a measure of localization quality to reduce the discrepancy. The combined score of classification and localization qualities serving as a box selection metric in non-maximum suppression well aligns with the proposed anchor assignment strategy and leads significant performance improvements. The proposed methods only add a single convolutional layer to RetinaNet baseline and does not require multiple anchors per location, so are efficient. Experimental results verify the effectiveness of the proposed methods. Especially, our models set new records for single-stage detectors on MS COCO test-dev dataset with various backbones.

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.

Citation

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