From 49ae43c5260959390a3affd8d3a94981b196c393 Mon Sep 17 00:00:00 2001 From: littletomatodonkey <2120160898@bit.edu.cn> Date: Wed, 13 May 2020 23:24:35 +0800 Subject: [PATCH] Add detailed doc for PSS-DET. (#659) * add detailed doc * add zh_CN doc of pssdet * fix link --- README.md | 1 + README_en.md | 1 + configs/rcnn_enhance/README.md | 36 ++++++++++++++++ configs/rcnn_enhance/README_en.md | 40 ++++++++++++++++++ ...ade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml | 0 ...ter_rcnn_dcn_r50_vd_fpn_3x_server_side.yml | 0 configs/rcnn_server_side_det/README.md | 13 ------ docs/images/pssdet.png | Bin 0 -> 72390 bytes 8 files changed, 78 insertions(+), 13 deletions(-) create mode 100644 configs/rcnn_enhance/README.md create mode 100644 configs/rcnn_enhance/README_en.md rename configs/{rcnn_server_side_det => rcnn_enhance}/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml (100%) rename configs/{rcnn_server_side_det => rcnn_enhance}/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml (100%) delete mode 100644 configs/rcnn_server_side_det/README.md create mode 100644 docs/images/pssdet.png diff --git a/README.md b/README.md index f23075d84..6755b510b 100644 --- a/README.md +++ b/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 diff --git a/README_en.md b/README_en.md index 9b7f12c68..597d83b08 100644 --- a/README_en.md +++ b/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 diff --git a/configs/rcnn_enhance/README.md b/configs/rcnn_enhance/README.md new file mode 100644 index 000000000..44f9ba043 --- /dev/null +++ b/configs/rcnn_enhance/README.md @@ -0,0 +1,36 @@ +## 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) | diff --git a/configs/rcnn_enhance/README_en.md b/configs/rcnn_enhance/README_en.md new file mode 100644 index 000000000..4add4dcef --- /dev/null +++ b/configs/rcnn_enhance/README_en.md @@ -0,0 +1,40 @@ +# Practical Server-side detection method base on RCNN + +## Introduction + + +* 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 + +| 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](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) | diff --git a/configs/rcnn_server_side_det/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml b/configs/rcnn_enhance/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml similarity index 100% rename from configs/rcnn_server_side_det/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml rename to configs/rcnn_enhance/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml diff --git a/configs/rcnn_server_side_det/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml b/configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml similarity index 100% rename from configs/rcnn_server_side_det/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml rename to configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml diff --git a/configs/rcnn_server_side_det/README.md b/configs/rcnn_server_side_det/README.md deleted file mode 100644 index c53d523c8..000000000 --- a/configs/rcnn_server_side_det/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Practical Server-side detection method base on RCNN - -## 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. - - -## Model Zoo - -| 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](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) | diff --git a/docs/images/pssdet.png b/docs/images/pssdet.png new file mode 100644 index 0000000000000000000000000000000000000000..e3b213eeae1209273435951b843f60dd8c478090 GIT binary patch literal 72390 zcma&O1yq#X9ydxOQUcPYgMx^Z$-5UCZ5MVd;jYfW7r$TmxOm|?xLZg5x$a@Qb9w*bVWnMaJz#8JTn>6 z0Rw(vK_y<GRwQZOyZLW zmH)8IF|>`82@H6H{;tT7C#x;gTI#1cPp|2k~}=2b3QYabj4eUoQ**P#en0*Ss+ zY}0R=pBOLKl{s>3Q=i+l6{jJ^KwuDIwV*qFFUR!EJoZEXh$z3Hn55QFXOy81QHBEB z9ZAIqK@y$Zgv#PCU>$|jlI;bC9yudyOJ1g&yx3f~f^J0KKRRqlFU?gor>g!ARwvq` zuA-N5bworFP2qHKJ@4q6q?Wl0gQmcsN*I*x5w6(Lh2}h8yyoAz7V^+MbB+G-R-8v+!`-w5f z7}@ei70wZ~gRPSH@JZGmf!sp7lRy=(q{hR#;UK55(B+B2kkqE*8eGJ^SbmUGVd%4a zvHhTmH&P{(v1X1d3{OG!UID`V;oT-A)Un?~WI#^wp~6oK)&QZoxqMZY1>+(;pP(z+$RW9AvSHtOuv?oW{_t z$Dk!Cldw=5ve+bPld7Z^2`QSKa*yQNxf+v`Fj>D{9j)b5Ol#?7OcJoV)R*kZq;2#B zDHA`E&ksPo(%CvvJ3W`7fuWZYQgcTezxj7{E>)z`C}Ka6ju$^446SV~^;ogF7yAoz zfJ+ifU;})>5$WaD%!yCMpo%`B`IBOV@8kGsSt*YDvEVt6!=TWnryxn5zIkOSyM2WI zjn1{a)b&%)HtD#?^vLQ9`=L*G$6G0nRs_gn@uh+iPbZRpfDZCQx0K)83Pp4if}YDA ziIxo&G7f}xmpg8PivPrL*Q?vQy^PBwz)pT(9Y;Cnv|<1gPTbP#tyuMw0T$vW&8$+x$9 zEW4-oJx^>%noE41c-mhoyY%v6bb-woFL>98_pl&pCPrUe!kb(eUTd__?iD1h`a2o; z%-r<1?OWD(@NmsuG?NoX{`S|$U>oH2^Q~w5E!rB%W-VsmGQ&UNHGO>Jch|WnfKbR| zQ{m`ZAIp3FYHp!;(|+ll{p@^}q5~Mh(4oYMO(bt5c~j>COEQ=IUcshf@q5|@ovZhv z8GG`6k{jP3oAfn_MMkVUtvaUZb;TnOt86zMqyhfIcAe92m%ZLA4~aCTCTRPFq3+Dt z{wthmZ<5o8cC>7yqDkY6!Zkp9AIY>QItY5GgsMBz&orKe zxn0{}?|kdxZhpLY`dCjck-2DIkfT^G?m&iRYv7CEMH+UOS8J(aTrK;ap3dCer~0Zr z0Z{8V6JOA7!r07d%NuIQw|&#?(q))o=GvsuLq-{HGv|44>eEVcoJ3L*-}svE(jE1( zWgU35;L*#a*vu0#yAFi5=AO~S|0ot>vzvYHT6DwGSu8ZBy*v77dMhZpJ)%^JcIlcE z^wS0Xv@q%VPPx}mO|TC;%VI;)*RGakO0>hUv7A%S>$ut-Mp@s1DU7Z3%*%AI_3H*} zvL4@vU{9JPR6y{R_L6NYMzbHs@o)&s&uQXjwXXEWeN^w*Pa9@w5lm0CUNS^=r}f&e zF_G&Nyc4oI^pkR>LB?+mTZ1Kb7g>VMei)ZR50#>h5esxj_9xo@^B8B2aO72;(?vFz$k@tZ$BpGZ znf3>G18w<-_kI{+CkHEEqnmdJDb8t3EE;bHZ%E3NW)3J`1mwZXf`u@&y!2sRMK7dBUEBUslZ-jEn0XDQ(eI3OUp!)jU*I-*vxG9Fjkpi$&(}p%7mw zNe}2&i*FRj;m-=TUx;$WBujdQx(RoV^tE#IwKvvwt{l@h^5s{05~cAh(<52+&f_|b zKL-G@WgRdOfcbmtioHDPv3k}-&}P_dWkc4){SVQNO3xYvxZ%fs?h_`ZTTo^5DRkmT zKfPpT>RosS$F*B%H@p)_y)^lSAKD8UDbDh=&2{c*HiznZwXp2yZRF%RZC$Uh-`w>% zW1c%O(>wFSjxIoy*DCF$w-J`b_yk_RsWx`=o%tbn&A$3;uHvV=!2BbRE8LR{6`PUR z>+S3IYXu(Z^YkUV=tkyOV#0PpA4Ug5!aC=Ot_5G6XF0A2Cq8PXEywL*e5Xq5cf+7$ zJdu0f^~`>*wrolA$DyM@)j0K(ig9z{lUkv2fsIy$xH*idY^1+m&R=nZsxCBYo}#n9 z`TE<1jm+Mgi{HdxNz?D!`Y8roYMvD5v8{WRAp1!xHTO$o-F>7U z1iqtVpNb0TVjcJB)JB#BuW)wJSE^An#7se4%{_bN0-EENXYM zeGq#syRL|wyT@1YsH;!+*P*G<-O=x(WX-!TVNf%-wcV#nT+3~D*1m@S(F0Lmt2$YOX8X!Q z-bTk1M2Y@$Z*wZhawAWFf~eR^ zUX|`;CVNQA(0D1Z!WuNUN4;yh0*WsfCwJv*qLXgs=jJzHy`tB?^D8hmxpD;21l5qG zqwmhuRVB2f1%verl@5&6`j40)sinJ*ckRDj3`ihnpsx-%q_r8oo}&oUkuYdP+_Tb+ zwCazOr^aF%4!=EoUWRYB1+7&J$4u^{Z%Yr(Uid)cY#U5!Iv3MvYgy!u4#c*v`j2J` z$N5I@;PTG~7w^9+&X{s3s^2c zi6)-&>q$HG^2CtZ;dokmW8L*s0{tXb#SS>jlE`l3V+eK^a_mVfN5M`xLtsrec!B2? zJ!gm8qi@iexfj|5*N;IrQt#TZL_q=2TESYs^S7$YA!+-&qg#xPxz58a^^Qp{1~k*J!W#e4K{MdljL)h zUJB`c3|wDoIs9qO*xl6V+`91N2Wk^$IgV7HrXpOIY$IE;Pn)6#Om94QaFL!;QVjCt zRu$HLE%{8!0j{?QV^_#WD)WA3**_cmF6I_nVjsSnt>n*jxO}mzDMDI9@2AHy zYmU^Z50#hrz~8xBdE=!So@AWC9<|cRi^D5>pC*BJVVf7Jr@AXN0^KD%f;G!_`TLaet|$E{*?YK zq;vTfHM}J`?C-{s@|58_79^JR$QN|dedw>ezb;HCrV~y}WevA{1PzEH>Q@l;V<0C8 zWXJ5@-p3?2Fl26%8#%K~U=sw)ZYaxfITyqQartM_!P*Bra71k(GyfUQiZTlMe9fig z)y(cj{O+`>PE4`Oy~;{{RQiUV>Nm-Es3K=h<+$0(->9z*7dk8TNZ9lFSHIw3lqF&9=Wp+G8+Z#n z6N;waknKpMHoRX8I=|Q_s+#cO7@O`t&n`Jqd}+(u_wI731_7>B)uq@1gE|5vhstf4 z`~xf=z?9o^Ku#Yga@gh)!H9bC>PhE!$EMHE4JNQ{ztSSvQH_R*vcuojrcc75C0$U{ zT+4k0B$u5%y#wM3ZcQ&pP4CA?=aVmtDJUtm zeiqc@V`nt5N6s`ovtiPf+Mjwy-anZ1@mTz_eyE`Y=2mLDV-9*wux$h(bvYv16HN;5 zHhd^VNh{3Xh2XLZVU(bGknf=v9@bx)guwChEtskz8VBo5F!||pTOem5@QO1({+j-| zp|b2#d)$?4BEf%r;__3f+!;?UIIhr0Vr1tZ@)5K;<)XrCnurlzpJ_WIoZ# zCAKh3!Qvy~9t>>nJ(P@{wUt_wW^_!mQ)xPGJvK=ktj3lD|Q!2DEYiOV0FwTz! 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