Add detailed doc for PSS-DET. (#659)

* add detailed doc
* add zh_CN doc of pssdet
* fix link

README.md

@@ -123,6 +123,7 @@
 - [车辆检测预训练模型](docs/featured_model/CONTRIB_cn.md)
 - [Objects365 2019 Challenge夺冠模型](docs/featured_model/CACascadeRCNN.md)
 - [Open Images 2019-Object Detction比赛最佳单模型](docs/featured_model/OIDV5_BASELINE_MODEL.md)
+- [服务器端实用目标检测模型](configs/rcnn_enhance/README.md): V100上速度20FPS时，COCO mAP高达47.8%。
 
 
 ## 许可证书

README_en.md

@@ -136,6 +136,7 @@ The following is the relationship between COCO mAP and FPS on Tesla V100 of repr
 - [YOLOv3 enhanced model](docs/featured_model/YOLOv3_ENHANCEMENT.md): Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 43.6%, and inference speed is improved as well
 - [Objects365 2019 Challenge champion model](docs/featured_model/CACascadeRCNN.md)
 - [Best single model of Open Images 2019-Object Detction](docs/featured_model/OIDV5_BASELINE_MODEL.md)
+- [Practical Server-side detection method](configs/rcnn_enhance/README_en.md): Inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%.
 
 
 ## License

configs/rcnn_enhance/README.md

+## 服务器端实用目标检测方案(Practical Server-side detection, PSS-DET)
+
+### 简介
+
+* 近年来，学术界和工业界广泛关注图像中目标检测任务。基于[PaddleClas](https://github.com/PaddlePaddle/PaddleClas)中SSLD蒸馏方案训练得到的ResNet50_vd预训练模型(ImageNet1k验证集上Top1 Acc为82.39%)，结合PaddleDetection中的丰富算子，飞桨提供了一种面向服务器端实用的目标检测方案PSS-DET(Practical Server Side Detection)。基于COCO2017目标检测数据集，V100单卡预测速度为为61FPS时，COCO mAP可达41.6%；预测速度为20FPS时，COCO mAP可达47.8%。
+
+* 以标准的Faster RCNN ResNet50_vd FPN为例，下表给出了PSS-DET不同的模块的速度与精度收益。
+
+| 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 |
+
+
+基于该实验结论，PaddleDetection结合Cascade RCNN，使用更大的训练与评估尺度(1000x1500)，最终在单卡V100上速度为20FPS，COCO mAP达47.8%。下图给出了目前类似速度的目标检测方法的速度与精度指标。
+
+
+![pssdet](../../docs/images/pssdet.png)
+
+**注意**
+> 这里为了更方便地对比，统一将V100的预测耗时乘以1.2倍，近似转化为Titan V的预测耗时。
+
+
+## 模型库
+
+| 骨架网络             | 网络类型       | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP |                           下载                          | 配置文件 |
+| :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :-------------: | :-----: |
+| ResNet50-vd-FPN-Dcnv2         | Faster     |     2     |   3x    |     61.425     |  41.6  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_server_side_det/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
+| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     20.001     |  47.8  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_server_side_det/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |

configs/rcnn_server_side_det/README.md → configs/rcnn_enhance/README_en.md

@@ -2,7 +2,34 @@
 
 ## Introduction
 
-* This is developed by PaddleDetection. Many useful tricks are utilized for the model training process. More details can be seen in the configuration file.
+
+* In recent years, object detection tasks have attracted widespread attention. [PaddleClas](https://github.com/PaddlePaddle/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.
+
+
+![pssdet](../../docs/images/pssdet.png)
+
+
+**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