4.2. General object detection

4.2.1. Practical Server-side detection method base on RCNN

4.2.1.1. 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

Based on the ablation experiments, Cascade RCNN and larger inference scale(1000x1500) are used for better performance. The final COCO mAP is 47.8% and the following figure shows mAP-Speed curves for some common detectors.

pssdet

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.

For more detailed information, you can refer to PaddleDetection.

4.2.2. Practical Mobile-side detection method base on RCNN

• This part is comming soon!