/DetectionTricks

ImageNet Detection Tricks

Primary LanguagePython

ImageNet Detection Tricks

ATLAB ImageNet leaderboard

model mAP eval method Training set PreTrain set Training Log Eval Log Base Module config
【ALL】= resnet101 (multiscale) + resnet101 ratio1:4,4:1 (multiscale) + resnet101 scale4 (multiscale) + resnet152 (multiscale) + inceptionv3 (multiscale) + rcnn_dcn (multiscale) 0.5295 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS1】=【ALL】 - dcn_rcnn (scale1000, mAP=0.4231) 0.5301 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS2】=【ENS1】 - dcn_rcnn (scale400, mAP=0.4249) 0.5297 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS3】=【ENS2】 - inceptionv3 (scale400, mAP=0.4257) 0.5289 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS4】=【ENS3】 - resnet152 (scale400, mAP=0.4318) - resnet101_ratio4 (scale400, mAP=0.4350) - resnet101_scale4 (scale400, mAP=0.4375) 0.5289 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS5】=【ALL】 - resnet152 (scale400, mAP=0.4318) - resnet101_ratio4 (scale400, mAP=0.4350) - resnet101_scale4 (scale400, mAP=0.4375) 0.5298 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS6】=【ENS1】 + dcn_rfcn (scale600, mAP=0.4695) 0.5305 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS7】=【ENS6】 - resnet101 (scale400, mAP=0.4456) 0.5295 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS8】=【ENS7】 - resnet101 (scale1000, mAP=0.4532) 0.5301 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS9】=【ENS8】 - resnet101_scale4 (scale1000, mAP=0.4555) 0.5296 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
【ENS10】=【ENS9】 - dcn_rfcn (scale800, mAP=0.4597) 0.5292 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
resnet101(multiscale) + resnet152 + inceptionv3 + rcnn_dcn 0.5238 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
resnet101(multiscale) +resnet152 +inceptionv3 0.5222 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
resnet101(multiscale) +deepmask +resnet152 +inceptionv3 0.5213 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
resnet101(multiscale) +deepmask +resnet152 0.5141 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
resnet101(multiscale) +deepmask 0.5090 nms+box voting Imagenet all - - - - NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1
Resnet-101 std 0.4874 test.py ImageNet all None Train Test Resnet-101 param Resnet 101 modeljson config
ResNet-101 smalldb 0.3958 test.py ImageNet train_0 None Train Eval Resnet-101 param Resnet 101 modeljson config

基础模型

使用多个差异很大的CNN模型 - diversity matters!

  • 7 * BN-Inception (32 Layers)
  • 2 * MSRA-Net (22 Layers)
  • ResNet, Identity Map

数据放大

  • random crop
  • multi-scale
  • contrast jittering
  • color jittering
  • Pretrain on LOC !!

单个模型的改进

  • Objectness loss
  • Negative categories
  • BBox Voting

训练技巧

  • Balanced Sampling
  • Multi-Scale Training
  • Online Hard Sample Mining

RPN Proposal

  • Cascade RPN
  • Constrained Neg/Pos Anchor Ratio

Pretraining

  • Pretrained Global Context

测试技巧

  • Multi-Scale Testing
  • HFlip
  • Box Votinng

Tricks的实现划分在以下5个文件夹中：

  • dataprocess
  • regionproposal
  • fastrcnn
  • postprocess
  • ensumble

上述代码尝试做成平台无关，与计算框架相关的代码都在PlatformRelated文件夹中