fast-reid/MODEL_ZOO.md

FastReID Model Zoo and Baselines

Introduction

BoT:

Bag of Tricks and A Strong Baseline for Deep Person Re-identification. CVPRW2019, Oral.

AGW:

This is a re-implementation of ReID-Survey with a Powerful AGW Baseline

SBS:

stronger baseline on top of BoT with tricks:

1. Non-local block
2. GeM pooling
3. Circle loss
4. Freeze backbone training
5. Cutout data augmentation & Auto Augmentation
6. Cosine annealing learning rate decay
7. Soft margin triplet loss

Market1501 Baselines

BoT:

Method	Pretrained	Rank@1	mAP	mINP
BoT(R50)	ImageNet	94.1%	85.9%	59.3%
BoT(R50-ibn)	ImageNet	-	-	-
BoT(S50)	ImageNet	-	-	-
BoT(R101-ibn)	ImageNet	-	-

AGW:

Method	Pretrained	Rank@1	mAP	mINP
AGW(R50)	ImageNet	94.9%	87.4%	63.1%
AGW(R50-ibn)	ImageNet	-	-	-
AGW(R101-ibn)	ImageNet	-	-	-

SBS:

Method	Pretrained	Rank@1	mAP	mINP
SBS(R50)	ImageNet	-	-	-
SBS(R50-ibn)	ImageNet	95.5%	88.7%	66.4%
SBS(S50)	ImageNet	-	-	-
SBS(R101-ibn)	ImageNet	-	-	-

DukeMTMC Baseline

BoT:

Method	Pretrained	Rank@1	mAP	mINP
BoT(R50)	ImageNet	86.1%	75.9%	38.7%
BoT(R50-ibn)	ImageNet	89.0%	78.8%	43.6%
BoT(S50)	ImageNet	-	-	-
BoT(R101-ibn)	ImageNet	-	-

AGW:

Method	Pretrained	Rank@1	mAP	mINP
AGW(R50)	ImageNet	88.9%	79.1%	43.2%
AGW(R50-ibn)	ImageNet	-	-	-
AGW(R101-ibn)	ImageNet	-	-	-

SBS:

Method	Pretrained	Rank@1	mAP	mINP
SBS(R50)	ImageNet	-	-	-
SBS(R50-ibn)	ImageNet	91.3%	81.6%	47.6%
SBS(S50)	ImageNet	-	-	-
SBS(R101-ibn)	ImageNet	-	-	-

MSMT17 Baseline

BoT:

Method	Pretrained	Rank@1	mAP	mINP
BoT(R50)	ImageNet	70.4%	47.5%	9.6%
BoT(R50-ibn)	ImageNet	76.9%	55.0%	13.5%
BoT(S50)	ImageNet	-	-	-
BoT(R101-ibn)	ImageNet	-	-

AGW:

Method	Pretrained	Rank@1	mAP	mINP
AGW(R50)	ImageNet	75.6%	52.6%	11.9%
AGW(R50-ibn)	ImageNet	-	-	-
AGW(R101-ibn)	ImageNet	-	-	-

SBS:

Method	Pretrained	Rank@1	mAP	mINP
SBS(R50)	ImageNet	-	-	-
SBS(R50-ibn)	ImageNet	84.2%	61.5%	15.7%
SBS(S50)	ImageNet	-	-	-
SBS(R101-ibn)	ImageNet	-	-	-

VeRi Baseline

BoT:

Method	Pretrained	Rank@1	mAP	mINP
BoT(R50-ibn)	ImageNet	96.1%	78.8%	43.8%

VehicleID Baseline

BoT: Method: BoT(R50-ibn)

Testset size	Pretrained	Rank@1	mAP	mINP
Small(800)	ImageNet	95.5%	89.5%	77.0%
Medium(1600)	ImageNet	93.8%	85.6%	69.4%
Large(2400)	ImageNet	93.3%	84.1%	67.4%

VERI-Wild Baseline

BoT: Method: BoT(R50-ibn)

Testset size	Pretrained	Rank@1	mAP	mINP
Small(3000)	ImageNet	96.4%	87.7%	69.2%
Medium(5000)	ImageNet	95.1%	83.5%	61.2%
Large(10000)	ImageNet	92.5%	77.3%	49.8%