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Graphing performance for a report.
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| 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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