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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.

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.

Practical Mobile-side detection method base on RCNN

  • This part is comming soon!