General object detection

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.

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.

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.

Practical Mobile-side detection method base on RCNN

• This part is comming soon!