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ResearchFormerly WizWand

Image Classification on LabelMe (test)

87.12AccuracyCoNAL
76.428879.204481.9884.7556Dec 24, 2020Jan 25, 2021Feb 26, 2021Mar 30, 2021May 1, 2021Jun 2, 2021Jul 5, 2021
Updated 8mo ago

Evaluation results

MethodLinks
CoNALBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1287.12
WeaSELaggregation=MIG2021.0786.36
MaxMIG2021.0785.45
Max-MIGBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1285.33
Snorkelaggregation=MIG2021.0785.15
AggNetBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1284.75
AggNet2021.0784.35
DL-CLBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1283.27
DL-GLADBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1283.12
Snorkelaggregation=CE2021.0782.89
CrowdLayer2021.0782.83
Anno-RegBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1282.77
DL-WCBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1282.74
WeaSELaggregation=CE2021.0782.46
Doctor NetBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1282.12
DoctorNet2021.0781.31
DL-MVBackbone=VGG-16, Data augmentation=true, Dropout=50%, Batch normalization=true2020.1279.83
Majority vote2021.0779.23
MBEM2021.0776.84