kleinxin/detectiontricks

ImageNet Detection Tricks

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

• ResNet-101 Variants

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