Person Re Identification On Market 1501 C

评估指标

Rank-1

mAP

mINP

评测结果

各个模型在此基准测试上的表现结果

模型名称	Rank-1	mAP	mINP	Paper Title
OS-Net	30.94	10.37	0.23	Omni-Scale Feature Learning for Person Re-Identification
Aligned++	31.00	10.95	0.32	AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
LGPR	27.72	8.26	0.24	A Person Re-identification Data Augmentation Method with Adversarial Defense Effect
CaceNet	42.92	18.24	0.67	Devil in the Details: Towards Accurate Single and Multiple Human Parsing
CIL (ResNet-50)	-	-	-	Benchmarks for Corruption Invariant Person Re-identification
MHN	33.29	10.69	0.38	Mixed High-Order Attention Network for Person Re-Identification
ABD-Net	29.65	9.81	0.26	ABD-Net: Attentive but Diverse Person Re-Identification
BDB	33.79	10.95	0.32	Batch DropBlock Network for Person Re-identification and Beyond
TDB	28.56	8.90	0.20	Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification
DG-Net	31.75	9.96	0.35	Joint Discriminative and Generative Learning for Person Re-identification
RRID	36.57	14.23	0.48	Relation Network for Person Re-identification
SBS (ResNet-50)	34.13	11.54	0.29	FastReID: A Pytorch Toolbox for General Instance Re-identification
TransReID	53.19	27.38	1.98	TransReID: Transformer-based Object Re-Identification
BoT (ResNet-50)	27.05	8.42	0.20	Bag of Tricks and A Strong Baseline for Deep Person Re-identification
MGN	29.56	9.72	0.29	Learning Discriminative Features with Multiple Granularities for Person Re-Identification
PCB	34.93	12.72	0.41	Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
AGW (ResNet-50)	31.90	12.13	0.35	Deep Learning for Person Re-identification: A Survey and Outlook
Pyramid	35.72	12.75	0.36	Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
LUPerson	-	-	-	Unsupervised Pre-training for Person Re-identification
PLR-OS	37.56	14.23	0.48	Learning Diverse Features with Part-Level Resolution for Person Re-Identification

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