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Performance on ImageNet validation set
        Luigi edited this page Oct 4, 2018 
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        16 revisions
      
    Results were obtained using (center cropped) images of the same size.
| Model | Version | Acc@1 | Acc@5 | 
|---|---|---|---|
| NASNet-A-Large | Tensorflow | 82.69 | 96.16 | 
| NASNet-A-Large | Our porting | 82.50 | 95.45 | 
| InceptionResNet-v2 | Tensorflow | 80.40 | 95.30 | 
| SENet154 | Caffe | 81.32 | 95.53 | 
| SENet154 | Our porting | 81.32 | 95.45 | 
| InceptionResNet-v2 | Our porting | 80.28 | 95.14 | 
| SE-ResNeXt101_32x4d | Our porting | 80.28 | 95.02 | 
| Inception-v4 | Tensorflow | 80.20 | 95.30 | 
| SE-ResNeXt101_32x4d | Caffe | 80.19 | 95.04 | 
| Inception-v4 | Our porting | 80.10 | 94.89 | 
| ResNeXt101_64x4d | Torch7 | 79.60 | 94.70 | 
| DualPathNet131 | Our porting | 79.44 | 94.60 | 
| DualPathNet98 | Our porting | 79.23 | 94.49 | 
| SE-ResNeXt50_32x4d | Our porting | 79.11 | 94.48 | 
| SE-ResNeXt50_32x4d | Caffe | 79.03 | 94.46 | 
| Xception | Keras | 79.00 | 94.50 | 
| ResNeXt101_64x4d | Our porting | 78.98 | 94.26 | 
| ResNeXt101_32x4d | Torch7 | 78.80 | 94.40 | 
| Xception | Our porting | 78.79 | 94.26 | 
| SE-ResNet152 | Caffe | 78.66 | 94.46 | 
| SE-ResNet152 | Our porting | 78.64 | 94.39 | 
| SE-ResNet101 | Our porting | 78.42 | 94.17 | 
| SE-ResNet101 | Caffe | 78.25 | 94.28 | 
| ResNet152 | Pytorch | 78.25 | 93.98 | 
| ResNeXt101_32x4d | Our porting | 78.22 | 93.94 | 
| FBResNet152 | Torch7 | 77.84 | 93.84 | 
| SE-ResNet50 | Caffe | 77.63 | 93.64 | 
| SE-ResNet50 | Our porting | 77.61 | 93.80 | 
| Inception-v3 | Pytorch | 77.50 | 93.59 | 
| FBResNet152 | Our porting | 77.44 | 93.54 | 
| ResNet101 | Pytorch | 77.31 | 93.56 | 
| DenseNet161 | Pytorch | 77.15 | 93.60 | 
| DenseNet201 | Pytorch | 76.93 | 93.39 | 
| CaffeResnet101 | Caffe | 76.40 | 92.90 | 
| CaffeResnet101 | Our porting | 76.11 | 92.70 | 
| ResNet50 | Pytorch | 76.01 | 92.93 | 
| DualPathNet68 | Our porting | 75.95 | 92.78 | 
| DenseNet169 | Pytorch | 75.63 | 92.81 | 
| DenseNet121 | Pytorch | 74.47 | 91.97 | 
| VGG19_BN | Pytorch | 74.22 | 91.85 | 
| NASNet-A-Mobile | Our porting | 74.10 | 91.78 | 
| NASNet-A-Mobile | Tensorflow | 74.00 | 91.60 | 
| BNInception | Our porting | 73.48 | 91.55 | 
| VGG16_BN | Pytorch | 73.48 | 91.54 | 
| ResNet34 | Pytorch | 73.27 | 91.43 | 
| VGG19 | Pytorch | 72.36 | 90.85 | 
| MobileNet-v2 | Pytorch | 71.81 | 90.41 | 
| VGG16 | Pytorch | 71.63 | 90.37 | 
| VGG13_BN | Pytorch | 71.62 | 90.36 | 
| VGG11_BN | Pytorch | 70.41 | 89.72 | 
| VGG13 | Pytorch | 69.98 | 89.31 | 
| ResNet18 | Pytorch | 69.64 | 88.98 | 
| MobileNet-v1 | Pytorch | 69.52 | 88.98 | 
| VGG11 | Pytorch | 68.87 | 88.66 | 
| ShuffleNet | Pytorch | 67.41 | 87.26 | 
| GoogLeNet | Our porting | 66.45 | 87.52 | 
| SqueezeNet-v1.1 | Pytorch | 58.18 | 80.51 | 
| SqueezeNet-v1.0 | Pytorch | 58.00 | 80.49 | 
| Alexnet | Pytorch | 56.62 | 79.06 |