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Last active February 5, 2023 17:19
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Graphing performance for a report.
Name Top 1 Error Mult-Adds Params Citation
NASNet-A 2.65 ? 2.76e7 zoph2017learning
NASNet-B 3.73 ? 2.6e6 zoph2017learning
NASNet-C 3.59 ? 3.1e6 zoph2017learning
DenseNet-40-12 5.24 ? 1e6 huang2016densely
DenseNet-100-12 4.1 ? 7e6 huang2016densely
DenseNet-100-24 3.74 ? 2.72e7 huang2016densely
DenseNet-100-40 3.46 ? 2.56e7 huang2016densely
ResNet-110 6.61 7.6e9 1.7e6 he2016deep
PNASNet-5 3.41 ? 3.2e6 liu2017progressive
AmoebaNet-A 3.34 ? 2.8e6 real2017large
AmoebaNet-B 3.37 ? 3.2e6 real2017large
DARTS 2.83 ? 3.4e6 liu2018darts
WRN-40-4 4.53 ? 8.9e6 zagoruyko2016wide
WRN-16-8 4.27 ? 11e6 zagoruyko2016wide
WRN-28-10 4.00 ? 3.65e7 zagoruyko2016wide
CondenseNet-94 5.00 1.22e8 3.3e5 huang2017condensenet
CondenseNet-86 5.00 6.5e7 5.2e5 huang2017condensenet
CondenseNet-160 3.46 1.084e9 3.1e6 huang2017condensenet
CondenseNet-182 3.76 5.13e8 4.2e6 huang2017condensenet
Moonshine-G(N/8) 5.06 4.558e5 8.64e7 crowley2017moonshine
Moonshine-BG(2-M/4) 6.03 1.657e5 2.82e7 crowley2017moonshine
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Name Top 1 Error Mult-Adds Params Citation
Conv MobileNet 28.3 4.866e9 2.93e7 howard2017mobilenets
MobileNet 29.4 5.69e8 4.2e6 howard2017mobilenets
0.75 MobileNet 31.25 3.25e8 2.6e6 howard2017mobilenets
0.5 MobileNet 36.3 1.49e8 1.3e6 howard2017mobilenets
0.25 MobileNet 49.4 4.1e7 5.0e5 howard2017mobilenets
GoogleNet 31.1 1.55e9 6.8e6 szegedy2015going
Squeezenet 42.5 1.7e9 1.25e6 iandola2016squeeze
ShuffleNet 32.4 1.4e8 1.878e6 zhang2017shuffle
ShuffleNet 2x 24.7 5.27e8 7.512e6 zhang2017shuffle
NASNet-A 26.0 5.64e8 5.3e6 zoph2017learning
NASNet-B 27.2 4.88e8 5.3e6 zoph2017learning
NASNet-C 27.5 5.56e8 4.9e6 zoph2017learning
PNASNet-5 25.8 5.88e8 5.1e6 liu2017progressive
AmoebaNet-A 25.5 5.55e8 5.1e6 real2017large
AmoebaNet-C 24.3 5.7e8 6.4e6 real2017large
DARTS 26.9 5.95e8 4.9e6 liu2018darts
Moonshine-G(4) 26.61 1.395e9 8.1e6 crowley2017moonshine
Moonshine-G(N) 32.98 5.59e8 3.1e6 crowley2017moonshine
DenseNet-121 25.02 0.6e10 0.9e7 huang2016densely
DenseNet-169 23.8 0.7e10 1.2e7 huang2016densely
DenseNet-201 22.58 0.8e10 2e7 huang2016densely
DenseNet-264 22.15 1.15e10 3.3e7 huang2016densely
WRN-50-2 21.9 1e10 6.89e7 zagoruyko2016wide
CondenseNet-8 29.0 2.74e8 2.9e6 huang2017condensenet
CondenseNet-4 26.2 5.29e8 4.8e6 huang2017condensenet
MobileNetV2 28.0 3e8 3.4e6 sandler2018inverted
MobileNetV2_1.4 25.3 5.85e8 6.9e6 sandler2018inverted
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