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36 lines
2.1 KiB
Markdown
36 lines
2.1 KiB
Markdown
# InsightFace
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Face Recognition Project
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### Experiments
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Default image size is 112x96 if not specified, all face images are aligned.
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In ResNet setting, \_v1 means original residual units. \_v2 means pre-activation units. \_v3 means BCBACB residual units. LResNet means we use conv33+stride11 in its first convoluition layer instead of common conv77+stride22 to preserve high image resolution. \_bo means using bottleneck residual units.
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In last several layers, some different options can be tried to determine how embedding layer looks like and it may affect the performance. The whole network architecture can be thought as {ConvLayers(->GlobalPool)->EmbeddingLayer->Softmax}. Embedding size is set to 512 expect for optionA, as embedding size in optionA is determined by the filter size of last convolution group.
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- OptionA: Use the final global pooling layer(GP) output as embedding layer directly.
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- OptionB: Use one FC layer after GP.
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- OptionC: Use FC->BN after GP.
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- OptionD: Use FC->BN->PRelu after GP.
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- OptionE: Use Dropout->FC->BN after last conv layer.
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- **Softmax on LFW**
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| Network/Dataset | VGG2@112x112 | WebFace | MS1M | - | - |
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| :-----------------: | :--------------: | :-----: | :--: | :--: | :--: |
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| SE-LResNet50E_v3 | 0.9973 | - | - | | |
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| SE-ResNet50C_v1 | 0.99217+-0.00236 | | | | |
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| SE-ResNet50B_v1 | Not Converged | | | | |
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| SE-ResNet50D_v1 | 0.99283+-0.00366 | | | | |
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| SE-ResNet50A\_v1 | | | | | |
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| SE-ResNet50E\_v1 | | | | | |
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| SE-LResNet50C_v1 | 0.99567+-0.00238 | | | | |
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| SE-LResNet50E_v1 | 0.99650+-0.00174 | - | - | | |
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| Inception-ResNet | - | - | - | | |
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| SE-Inception-ResNet | - | - | - | | |
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| MobileNet | - | - | - | | |
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| ResNeXt | - | - | - | | |
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