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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 |
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 |