code_test/configs/pisa

• Prime Sample Attention in Object Detection
• • Introduction
  • Results and models

Prime Sample Attention in Object Detection

Introduction

@inproceedings{cao2019prime,
  title={Prime sample attention in object detection},
  author={Cao, Yuhang and Chen, Kai and Loy, Chen Change and Lin, Dahua},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}

Results and models

PISA	Network	Backbone	Lr schd	box AP	mask AP	Config	Download
×	Faster R-CNN	R-50-FPN	1x	36.4		-
√	Faster R-CNN	R-50-FPN	1x	38.4		config	model | log
×	Faster R-CNN	X101-32x4d-FPN	1x	40.1		-
√	Faster R-CNN	X101-32x4d-FPN	1x	41.9		config	model | log
×	Mask R-CNN	R-50-FPN	1x	37.3	34.2	-
√	Mask R-CNN	R-50-FPN	1x	39.1	35.2	config	model | log
×	Mask R-CNN	X101-32x4d-FPN	1x	41.1	37.1	-
√	Mask R-CNN	X101-32x4d-FPN	1x
×	RetinaNet	R-50-FPN	1x	35.6		-
√	RetinaNet	R-50-FPN	1x	36.9		config	model | log
×	RetinaNet	X101-32x4d-FPN	1x	39.0		-
√	RetinaNet	X101-32x4d-FPN	1x	40.7		config	model | log
×	SSD300	VGG16	1x	25.6		-
√	SSD300	VGG16	1x	27.6		config	model | log
×	SSD300	VGG16	1x	29.3		-
√	SSD300	VGG16	1x	31.8		config	model | log

Notes:

• In the original paper, all models are trained and tested on mmdet v1.x, thus results may not be exactly the same with this release on v2.0.
• It is noted PISA only modifies the training pipeline so the inference time remains the same with the baseline.