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Performance on ImageNet validation set
Luigi edited this page Oct 4, 2018
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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 |
| SENet154 | Caffe | 81.32 | 95.53 |
| SENet154 | Our porting | 81.32 | 95.45 |
| InceptionResNetV2 | Tensorflow | 80.40 | 95.30 |
| InceptionV4 | Tensorflow | 80.20 | 95.30 |
| SE-ResNeXt101_32x4d | Our porting | 80.236 | 95.028 |
| SE-ResNeXt101_32x4d | Caffe | 80.19 | 95.04 |
| InceptionResNetV2 | Our porting | 80.170 | 95.234 |
| InceptionV4 | Our porting | 80.062 | 94.926 |
| DualPathNet107_5k | Our porting | 79.746 | 94.684 |
| ResNeXt101_64x4d | Torch7 | 79.6 | 94.7 |
| DualPathNet131 | Our porting | 79.432 | 94.574 |
| DualPathNet92_5k | Our porting | 79.400 | 94.620 |
| DualPathNet98 | Our porting | 79.224 | 94.488 |
| SE-ResNeXt50_32x4d | Our porting | 79.076 | 94.434 |
| SE-ResNeXt50_32x4d | Caffe | 79.03 | 94.46 |
| Xception | Keras | 79.000 | 94.500 |
| ResNeXt101_64x4d | Our porting | 78.956 | 94.252 |
| Xception | Our porting | 78.888 | 94.292 |
| ResNeXt101_32x4d | Torch7 | 78.8 | 94.4 |
| SE-ResNet152 | Caffe | 78.66 | 94.46 |
| SE-ResNet152 | Our porting | 78.658 | 94.374 |
| ResNet152 | Pytorch | 78.428 | 94.110 |
| SE-ResNet101 | Our porting | 78.396 | 94.258 |
| SE-ResNet101 | Caffe | 78.25 | 94.28 |
| ResNeXt101_32x4d | Our porting | 78.188 | 93.886 |
| FBResNet152 | Torch7 | 77.84 | 93.84 |
| SE-ResNet50 | Caffe | 77.63 | 93.64 |
| SE-ResNet50 | Our porting | 77.636 | 93.752 |
| DenseNet161 | Pytorch | 77.560 | 93.798 |
| ResNet101 | Pytorch | 77.438 | 93.672 |
| FBResNet152 | Our porting | 77.386 | 93.594 |
| InceptionV3 | Pytorch | 77.294 | 93.454 |
| DenseNet201 | Pytorch | 77.152 | 93.548 |
| DualPathNet68b_5k | Our porting | 77.034 | 93.590 |
| 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 | Tensorflow | 74.00 | 91.60 |
| NASNet-A-Mobile | Our porting | 74.10 | 91.78 |
| 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 | Our porting | 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 | Our porting | 69.52 | 88.98 |
| VGG11 | Pytorch | 68.87 | 88.66 |
| GoogLeNet | Our porting | 66.45 | 87.52 |
| SqueezeNet1_1 | Pytorch | 58.18 | 80.51 |
| SqueezeNet1_0 | Pytorch | 58.00 | 80.49 |
| Alexnet | Pytorch | 56.62 | 79.06 |