# Face-Alignment

**Repository Path**: ideask/Face-Alignment

## Basic Information

- **Project Name**: Face-Alignment
- **Description**: 「Pytorch」Try to resolve Face Alignment problems by LinearModel,ResNet18,MobileNetV2(PFLD)
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-07-02
- **Last Updated**: 2021-02-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Face Alignment

### Introduction

Using Pytorch as a framework, based on Linear model,ResNet18 or MobileNetV2
#### 1. Train Linear Model:
- Data preparation：
  - Run ` python ./Data/ODATA/linear.py `
- Training steps：
  - Run `tensorboard --logdir=/home/kenny/Desktop/Face-Alignment/CheckPoints/tensorboard_linear &`
  - Run `python Train_linear.py -h` get usage
  - Run default parms `python Train_linear.py`
  - Checkpoint `checkpoint_epoch_x.pth.tar`in`./CheckPoints/snapshot_linear/`
  - You can get training log file from `./CheckPoints/train_linear.logs`
  
- Testing steps：
  - Run `python Test_linear.py -h` get usage
  - Run default parms `python Test_linear.py`
  
#### 2. Train ResNet18 Model:
- Data preparation：
  - Run ` python ./Data/ODATA/resnet.py `
- Training steps：
  - Run `tensorboard --logdir=/home/kenny/Desktop/Face-Alignment/CheckPoints/tensorboard_resnet &`
  - Run `python Train_resnet.py -h` get usage
  - Run default parms `python Train_resnet.py`
  - Checkpoint `checkpoint_epoch_x.pth.tar`in`./CheckPoints/snapshot_resnet/`
  - You can get training log file from `./CheckPoints/train_resnet.logs`
  
- Testing steps：
  - Run `python Test_resnet.py -h` get usage
  - Run default parms `python Test_resnet.py` 
  
#### 3. Train MobileNetV2 Model(refer to PFLD):
- Data preparation：
  - Run ` python ./Data/ODATA/pfld.py `
- Training steps：
  - Run `tensorboard --logdir=/home/kenny/Desktop/Face-Alignment/CheckPoints/tensorboard_pfld &`
  - Run `python Train_pfld.py -h` get usage
  - Run default parms `python Train_pfld.py`
  - Checkpoint `checkpoint_epoch_x.pth.tar`in`./CheckPoints/snapshot_pfld/`
  - You can get training log file from `./CheckPoints/train_pfld.logs`
  
- Testing steps：
  - Run `python Test_pfld.py -h` get usage
  - Run default parms `python Test_pfld.py` 
  
### Result
##### Predict landmarks:Green Points
##### Ground Truth landmarks:Red Points
#### Linear Model:
- Loss

  ![](./Results/LinearModel/loss.png)

- Predict

  ![](./Results/LinearModel/result1.png)
  ![](./Results/LinearModel/result2.png)
#### ResNet18:
- Loss

  ![](./Results/ResNetModel/loss.png)

- Predict

  ![](./Results/ResNetModel/result1.png)
  ![](./Results/ResNetModel/result2.png)
#### MobileNetV2(refer to PFLD):
- Loss

  ![](./Results/PfldModel/loss.png)

- Predict

  ![](./Results/PfldModel/result.png)
### Reference

- [PFLD Tensorflow Implementation](https://github.com/guoqiangqi/PFLD)
- [PFLD pytorch Implementation](https://github.com/polarisZhao/PFLD-pytorch)
- [pytorch](https://github.com/pytorch/pytorch)
- [pytorch-book](https://github.com/chenyuntc/pytorch-book)