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https://github.com/yxlijun/Pyramidbox.pytorch

Pyramidbox implement with pytorch
https://github.com/yxlijun/Pyramidbox.pytorch

face-detection pyramidbox vgg

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Pyramidbox implement with pytorch

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## PyramidBox: A Context-assisted Single Shot Face Detector.##
[A PyTorch Implementation of PyramidBox](https://arxiv.org/abs/1803.07737)

### Description
I train pyramidbox with pytorch and the result approaches the original paper result,the pretrained model can be downloaded in [vgg](https://pan.baidu.com/s/1Q-YqoxJyqvln6KTcIck1tQ),the final model can be downloaded in [Pyramidbox](https://pan.baidu.com/s/1VtzgB9srkJY4SUtVM3n8tw).the AP in WIDER FACE as following:

| | Easy MAP | Medium MAP | hard MAP |
| -------- | ---------|------------| --------- |
| origin paper| 0.960 | 0.948 | 0.888 |
| this repo | 0.948 | 0.938 | 0.880 |

the AP in AFW,PASCAL,FDDB as following:

| AFW | PASCAL | FDDB |
| --------- |-----------| ---------|
| 99.65 | 99.02 | 0.983 |

the gap is small with origin paper,I train 120k batch_size 4 which is different from paper,which maybe cause the gap,if you have more gpu ,the final result maybe better.

### Requirement
* pytorch 0.3
* opencv
* numpy
* easydict

### Prepare data
1. download WIDER face dataset
2. modify data/config.py
3. ``` python prepare_wider_data.py```

### Train
```
python train.py --batch_size 4
--lr 5e-4
```

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

### Result


demo
demo

### References
* [PyramidBox: A Context-assisted Single Shot Face Detector](https://arxiv.org/abs/1803.07737)
* [PyramidBox model](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/face_detection)
* [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch)