轻量级神经网络准确率、Params、MAdds、推理时间等对比，对比数据集:ImageNet 2012 classification dataset。
| Date | Model | Detail | Top-1 Acc. (%) | Top-5 Acc. (%) | Params(M) | MAdds(M) | Infer-time(ms) |
| 2016.2 | SqueezeNet | 67.5 | 88.2 | 3.2 | 708 | ||
| 2016.8 | DenseNet | DenseNet(0.5) | 41.4 | 42 | 25 | ||
| DenseNet(1.0) | 44.8 | 142 | 63 | ||||
| DenseNet(1.5) | 60.1 | 295 | 103 | ||||
| DenseNet(2.0) | 65.4 | 519 | 164 | ||||
| 2016.1 | Xception | Xception(0.5) | 55.1 | 40 | 19 | ||
| Xception(1.0) | 65.9 | 145 | 51 | ||||
| Xception(1.5) | 70.6 | 305 | 95 | ||||
| Xception(2.0) | 72.4 | 525 | 149 | ||||
| 2017.4 | MobileNet v1 | MobileNet v1(0.25) | 50.6 | 0.5 | 41 | 27 | |
| MobileNet v1(0.5) | 63.7 | 1.3 | 149 | 60 | |||
| MobileNet v1(0.75) | 68.4 | 2.6 | 325 | 94 | |||
| MobileNet v1(1.0) | 70.6 | 89.5 | 4.2 | 569 | 154 | ||
| 2017.7.10 | IGCV | ||||||
| 2017.7.21 | NASNet | NASNet-A | 74 | 91.3 | 5.3 | 564 | 183 |
| 2017.11 | CondenseNet | CondenseNet(G=C=4) | 71 | 90 | 2.9 | 274 | |
| CondenseNet(G=C=8) | 73.8 | 91.7 | 4.8 | 529 | |||
| 2017.12 | PNASNet | PNASNet | 74.2 | 91.9 | 5.1 | 588 | |
| 2017.9 | SENet | ||||||
| 2017.12 | ShuffleNet v1 | ShuffleNet(0.5) | 56.8 | 38 | 18 | ||
| ShuffleNet v1(1.0)-g=3 | 67.4 | 140 | 46 | ||||
| ShuffleNet v1(1.5)-g=3 | 71.5 | - | 3.4 | 292 | 97 | ||
| ShuffleNet v1(x2)-g=3 | 73.7 | - | 5.4 | 524 | 156 | ||
| 2018.1 | MobileNet v2 | MobileNet v2(0.35) | 60.8 | 1.6 | 59.2 | 16.6/19.6/13.9(Pixel*) | |
| MobileNet v2(1.0) | 72 | 91 | 3.4 | 300 | 75(Pixel 1 Phone) | ||
| MobileNet v2(1.4) | 74.7 | 92.5 | 6.9 | 585 | 143(Pixel 1 Phone) | ||
| 2018.2 | AmoebaNet | AmoebaNet-A | 74.5 | 92 | 5.1 | 555 | 190 |
| 2018.4 | IGCV2 | IGCV2-0.25 | 54.9 | 0.5 | 46 | 32 | |
| IGCV2-0.5 | 65.5 | 1.3 | 156 | 65 | |||
| IGCV2-1.0 | 70.7 | 4.1 | 564 | 204 | |||
| 2018.6 | IGCV3 | IGCV3-0.7 | 68.45 | 2.8 | 210 | 85 | |
| IGCV3-1.0 | 72.2 | 3.5 | 318 | 159 | |||
| IGCV3-1.4 | 74.55 | 7.2 | 610 | 222 | |||
| 2018.7 | ShuffleNet v2 | ShuffleNet v2(0.5) | 60.3 | 1.4 | 41 | 18 | |
| ShuffleNet v2(1.0) | 69.4 | 2.3 | 146 | 41 | |||
| ShuffleNet v2(1.5) | 72.6 | 3.5 | 299 | 85 | |||
| ShuffleNet v2(x2) | 74.9 | 7.4 | 597 | 149 | |||
| ShuffleNet v2(x2)-SE | 75.4 | 597 | 179 | ||||
| 2019.3 | MnasNet | MnasNet-Small | 64.9 | 1.9 | 65.1 | 20.3/24.2/17.2 | |
| MnasNet-A1 | 75.2 | 92.5 | 3.9 | 312 | 78(Pixel 1 Phone) | ||
| MnasNet-A2 | 75.6 | 92.7 | 4.8 | 340 | 84(Pixel 1 Phone) | ||
| MnasNet-A3 | 76.7 | 93.3 | 5.2 | 403 | 103(Pixel 1 Phone) | ||
| 2019.5.6 | MobileNet v3 | MobileNet v3-Large(1.0) | 75.2 | 5.4 | 219 | 51/61/44(Pixel*) | |
| MobileNet v3-Large(0.75) | 73.3 | 4 | 155 | 39/46/40(Pixel*) | |||
| MobileNet v3-Small(1.0) | 67.4 | 2.5 | 56 | 15.8/19.4/14.4(Pixel*) | |||
| MobileNet v3-Small(0.75) | 65.4 | 2 | 44 | 12.8/15.6/11.7(Pixel*) |
以上就是关于轻量级神经网络算法的对比结果，点击Model列的算法可以详细了解各个算法。