| Model | Dataset | #Params | Epochs | Test Prec(Paper) | Test Prec(This impl) |
|---|---|---|---|---|---|
| ResNet-20 | CIFAR-10 | 0.27M | 140,180,200 | 91.25% | 92.02% |
| ResNet-32 | CIFAR-10 | 0.46M | 140,180,200 | 92.49% | 92.61% |
| ResNet-44 | CIFAR-10 | 0.66M | 100,150,200 | 92.83% | 92.46% |
| ResNet-56 | CIFAR-10 | 0.85M | 100,150,200 | 93.03% | 93.22% |
| ResNet-110 | CIFAR-10 | 1.73M | 82,123,164 | 93.57% | 93.40% |
| ResNet-18(A) | CIFAR-10 | 11.0M | 100,150,200 | - | 93.54% |
| ResNet-18(B) | CIFAR-10 | 11.17M | 80,110,120 | - | 94.51% |
| ResNet-50 | CUB-200 | 23.92M | 26,36,40 | - | 81.74% |
| ResNeXt-50 | CUB-200 | 23.39M | 26,36,40 | - | 82.70% |
| RegNetX-4.0 | CUB-200 | 21.03M | 26,36,40 | - | 84.31% |
| RegNetY-8.0 | CUB-200 | 37.57M | 26,36,40 | - | 84.40% |
| RegNetY-8.0 | CUB-200 | 37.57M | cos60+wp | - | 84.86% |
| RegNetY-32.0 | CUB-200 | 142.08M | cos60 | - | 85.23% |
| EfficientNet-B2 | CUB-200 | 7.98M | 26,36,40 | - | 82.60% |
| RegY32+EB2+NeXt50 | CUB-200 | - | - | - | 87.31% |
For backbone networks
Most of backbone networks already have pytorch official version(ResNet.etc), my implementations have a little diffience with them because of my programming habits
For other networks
Some networks don't have offical pytorch version for several reasons(author didn't public the code.etc), my implementations are totally original reproductions
Common Type:
- AlexNet 2012
- NIN 2013
- VGG 2014
- Inception/GoogLeNet 2014
- InceptionV2/V3 2015
- InceptionV4/Res 2016
- ResNet 2015
- ResNetV2 2016
- ResNeXt 2016
- DenseNet 2016
- SENet 2017
- MnasNet 2018
- EfficientNet 2019
Light Type:
- ShuffleNet 2017
- MobileNet 2017
Other:
- BasicGNN
- OpenPose 2015
- Hourglass 2015
- GNNlikeCNN 2015
- SimpleBaseline 2017
- CPN 2017
- OpenPose 2017
- FPNPoseNet 2017
- CPN+GNN 2018
- Darknet-19 2016
- Darknet-53 2018
Will release a new repertory in the future (not in current repertory)
- Focusloss 2016
- labelsmooth