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ResearchFormerly WizWand

Pedestrian Attribute Recognition on RAP

81.87mAALM
69.171672.468375.76579.0617Sep 28, 2017Mar 8, 2018Aug 17, 2018Jan 26, 2019Jul 6, 2019Dec 15, 2019May 25, 2020
Updated 8mo ago

Evaluation results

MethodLinks
ALM#P=17.1M, GFLOPs=1.952019.1081.8768.1774.7186.4880.16
ALMBackbone=BN-Inception2020.0581.8768.1774.7186.4880.16
GRL#P=>50M, GFLOPs=>102019.1081.2—77.780.979.29
LG-Net#P=>20M, GFLOPs=> 42019.1078.686880.3679.8280.09
LGNetBackbone=Inception-V22020.0578.686880.3679.8280.09
BaselineBackbone=ResNet502020.0578.4867.1782.8476.2578.94
JRL2019.1077.81—78.1178.9878.58
VeSPA#P=17.0M, GFLOPs=> 32019.1077.767.3579.5179.6779.59
FocalLossBackbone=ResNet502020.0577.3265.9180.7475.1376.47
HP-netModel type=HydraPlus-Net2017.0976.1265.3977.3378.7978.05
HP-Net2019.1076.1265.3977.3378.7978.05
HPNetBackbone=InceptionNet2020.0576.1265.3977.3378.7978.05
BN-Inception#P=10.3M, GFLOPs=1.782019.1075.7665.5778.9277.4978.2
JRLmode=single model version2019.1074.74—75.0874.9674.62
JRLBackbone=AlexNet2020.0574.74—75.0874.9674.62
M-netModel type=CNN2017.0974.4464.9977.8377.8977.86
PGDM#P=87.2M, GFLOPs=≈12019.1074.3164.5778.8675.977.35
PGDMBackbone=CaffeNet2020.0574.3164.5778.8675.977.35
DeepMarModel type=CNN2017.0973.7962.0274.9276.2175.56
DeepMar#P=58.5M, GFLOPs=0.722019.1073.7962.0274.9276.2175.56
DeepMARBackbone=CaffeNet2020.0573.7962.0274.9276.2175.56
FC6-mmFeatures=Deep-learned2017.0973.3233.3737.5773.2349.66
FC7-mmFeatures=Deep-learned2017.0972.2831.7235.7571.7847.73
ELF-mmFeatures=Hand-crafted2017.0969.9429.2932.8471.1844.95
ACNModel type=CNN2017.0969.6662.6180.1272.2675.98
ACN2019.1069.6662.6180.1272.2675.98