PulseFocusPlatform/static/configs/rcnn_enhance

Practical Server-side detection method base on RCNN

Introduction

• In recent years, object detection tasks have attracted widespread attention. PaddleClas open-sourced the ResNet50_vd_SSLD pretrained model based on ImageNet(Top1 Acc 82.4%). And based on the pretrained model, PaddleDetection provided the PSS-DET (Practical Server-side detection) with the help of the rich operators in PaddleDetection. The inference speed can reach 61FPS on single V100 GPU when COCO mAP is 41.6%, and 20FPS when COCO mAP is 47.8%.

• We take the standard Faster RCNN ResNet50_vd FPN as an example. The following table shows ablation study of PSS-DET.

Trick	Train scale	Test scale	COCO mAP	Infer speed/FPS
baseline	640x640	640x640	36.4%	43.589
+test proposal=pre/post topk 500/300	640x640	640x640	36.2%	52.512
+fpn channel=64	640x640	640x640	35.1%	67.450
+ssld pretrain	640x640	640x640	36.3%	67.450
+ciou loss	640x640	640x640	37.1%	67.450
+DCNv2	640x640	640x640	39.4%	60.345
+3x, multi-scale training	640x640	640x640	41.0%	60.345
+auto augment	640x640	640x640	41.4%	60.345
+libra sampling	640x640	640x640	41.6%	60.345

And the following figure shows mAP-Speed curves for some common detectors.

Note

For fair comparison, inference time for PSS-DET models on V100 GPU is transformed to Titan V GPU by multiplying by 1.2 times.

Model Zoo

COCO dataset

Backbone	Type	Image/gpu	Lr schd	Inf time (fps)	Box AP	Mask AP	Download	Configs
ResNet50-vd-FPN-Dcnv2	Faster	2	3x	61.425	41.6	-	model	config
ResNet50-vd-FPN-Dcnv2	Cascade Faster	2	3x	20.001	47.8	-	model	config
ResNet101-vd-FPN-Dcnv2	Cascade Faster	2	3x	19.523	49.4	-	model	config

Attention: Pretrained models whose congigurations are in the directory generic just support inference but do not support training and evaluation as now.