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https://github.com/jianzhnie/s3fd_pytorch

pytorch 实现的S3FD,对原来的代码进行了优化,更简洁高效
https://github.com/jianzhnie/s3fd_pytorch

facedetection s3fd ssd

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pytorch 实现的S3FD,对原来的代码进行了优化,更简洁高效

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## S³FD: Single Shot Scale-invariant Face Detector ##
A PyTorch Implementation of Single Shot Scale-invariant Face Detector

### Description
Meanwhile train hand and head with S3FD,hand dataset is [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/),head dataset is [SCUT-HEAD](https://github.com/HCIILAB/SCUT-HEAD-Dataset-Release),we can download [hand model](https://pan.baidu.com/s/1_d4HqioBJknGj2ypwtYaXg) and [face model](https://pan.baidu.com/s/1epyTAUc6qSt3oZ7veK4oEw)

### Requirement
* pytorch 1.3.0
* opencv
* numpy
* easydict

### Prepare data
1. download WIDER face dataset、Egohands dataset and SCUT-HEAD
2. modify data/config.py
3. ``` python prepare_wider_data.py ```

### Train
We can choose different dataset to train different target[face,head,hand]
```
python main.py --batch_size 4 --dataset face\hand\head
```

### Evalution
according to yourself dataset path,modify data/config.py
1. Evaluate on AFW.
```
python afw_test.py
```
2. Evaluate on FDDB
```
python fddb_test.py
```
3. Evaluate on PASCAL face
```
python pascal_test.py
```
4. test on WIDER FACE
```
python wider_test.py
```
### Demo
you can test yourself image
```
python demo.py
```

### Result
1. AFW PASCAL FDDB


afw
pascal
fddb

AFW AP=99.81 paper=99.85
PASCAL AP=98.77 paper=98.49
FDDB AP=0.975 paper=0.983
WIDER FACE:
Easy AP=0.925 paper = 0.927
Medium AP=0.925 paper = 0.924
Hard AP=0.854 paper = 0.852

2. demo


afw

### References
* [S³FD: Single Shot Scale-invariant Face Detector](https://arxiv.org/abs/1708.05237)
* [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch)