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Update README.md

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Jia Guo
2017-12-07 16:29:51 +08:00
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@@ -5,7 +5,7 @@ Face Recognition Project
### How to use
1. Download pre-aligned training dataset from our data repo which is in MXnet .rec file format(maybe ready soon), or align your dataset by yourself and then pack them to prevent random small files accessing. Check those scripts under src/common and src/align.
1. Download pre-aligned training dataset from our data repo which is a large binary file in MXnet .rec format(maybe ready soon), or align your dataset by yourself and then pack them to prevent random small files accessing. Check those scripts under src/common and src/align.
2. Run src/train_softmax.py to train your model and set proper parameters. For example, loss-type=0 means pure softmax while loss-type=1 means SphereLoss. It will output LFW accuracy every 2000 batches and save the model if necessary.
### Notes
@@ -16,13 +16,13 @@ In ResNet setting, \_v1 means original residual units. \_v2 means pre-activatio
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.
- OptionA: Use the final global pooling layer(GP) output as embedding layer directly.
- OptionXD: Same with OptionD but use dropout after GP. OptionAD is the default setting for inception series networks.
- OptionA: Use global pooling layer(GP). This is the default setting for all networks except inceptions.
- OptionB: Use one FC layer after GP.
- OptionC: Use FC->BN after GP.
- OptionD: Use FC->BN->PRelu after GP.
- OptionE: Use Dropout->FC->BN after last conv layer.
- OptionF: Use Dropout->FC->BN->PRelu after last conv layer.
- OptionG: Use Dropout->FC after GP.
- OptionE: Use BN->Dropout->FC->BN after last conv layer.
- OptionF: Use BN->PRelu->Dropout->FC->BN after last conv layer.
@@ -34,29 +34,31 @@ In last several layers, some different options can be tried to determine how emb
- **Softmax only on VGG2@112x112**
| Network/Dataset | LFW | ------ | ------ |
| :--------------------: | :--------------: | :----: | :----: |
| ResNet50D_v1 | 0.99350+-0.00293 | | |
| SE-ResNet50A\_v1 | 0.99367+-0.00233 | | |
| SE-ResNet50B_v1 | 0.99200+-0.00407 | | |
| SE-ResNet50C_v1 | 0.99317+-0.00404 | | |
| SE-ResNet50D_v1 | 0.99383+-0.00259 | | |
| SE-ResNet50E\_v1 | 0.99267+-0.00343 | | |
| SE-ResNet50F\_v1 | 0.99367+-0.00194 | | |
| SE-LResNet50C_v1 | 0.99567+-0.00238 | | |
| SE-LResNet50D_v1 | 0.99600+-0.00281 | | |
| SE-LResNet50E_v1 | 0.99650+-0.00174 | - | - |
| SE-LResNet50A_v3 | 0.99583+-0.00327 | | |
| SE-LResNet50D_v3 | 0.99617+-0.00358 | - | - |
| SE-LResNet50E_v3 | 0.99767+-0.00200 | - | - |
| SE-LResNet50F_v3 | 0.99617+-0.00358 | | |
| SE-LResNet50G_v3 | 0.99350+-0.00263 | | |
| SE-ResNet101D_v3 | 0.99517+-0.00252 | | |
| SE-ResNet101E_v3 | 0.99467+-0.00221 | | |
| Inception-ResNet-v2 | - | - | - |
| SE-Inception-ResNet-v2 | - | - | - |
| MobileNetD | 0.99150+-0.00425 | - | - |
| MobileNetE | | | |
| LMobileNetD | | | |
| LMobileNetE | | | |
| ResNeXt | - | - | - |
| Network/Dataset | LFW | ------ | ------ |
| :--------------------: | :--------------: | :--------------: | :--------------: |
| ResNet50D_v1 | 0.99350+-0.00293 | | |
| SE-ResNet50A\_v1 | 0.99367+-0.00233 | | |
| SE-ResNet50B_v1 | 0.99200+-0.00407 | | |
| SE-ResNet50C_v1 | 0.99317+-0.00404 | | |
| SE-ResNet50D_v1 | 0.99383+-0.00259 | | |
| SE-ResNet50E\_v1 | 0.99267+-0.00343 | | |
| SE-ResNet50F\_v1 | 0.99367+-0.00194 | | |
| SE-LResNet50C_v1 | 0.99567+-0.00238 | | |
| SE-LResNet50D_v1 | 0.99600+-0.00281 | | |
| SE-LResNet50E_v1 | 0.99650+-0.00174 | - | - |
| SE-LResNet50A_v3 | 0.99583+-0.00327 | | |
| SE-LResNet50D_v3 | 0.99617+-0.00358 | - | - |
| SE-LResNet50E_v3 | 0.99767+-0.00200 | - | - |
| LResNet50E_v3 | | | |
| SE-LResNet50F_v3 | | | |
| SE-LResNet50G_v3 | 0.99350+-0.00263 | | |
| SE-ResNet101D_v3 | 0.99517+-0.00252 | | |
| SE-ResNet101E_v3 | 0.99467+-0.00221 | | |
| SE-ResNet152E_v3 | 0.99500+-0.00307 | | |
| Inception-ResNetAD | 0.99417+-0.00375 | - | - |
| SE-Inception-ResNet-v2 | - | - | - |
| MobileNetD | 0.99150+-0.00425 | - | - |
| LMobileNetD | | | |
| LMobileNetE | 0.99600+-0.00281 | - | - |
| LMobileNetF | | | |
| LResNeXt101E_v3 | | | |