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Jia Guo
2019-05-03 13:03:55 +08:00
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### RetinaFace Face Detector
# RetinaFace Face Detector
## Introduction
RetinaFace is a robust single stage face detector which initially described as an [arXiv technical report](https://arxiv.org/abs/1905.00641)
## Training
1. Download groundtruth labels from [baiducloud](https://pan.baidu.com/s/1Laby0EctfuJGgGMgRRgykA) or dropbox and organise the dataset directory under ``insightface/RetinaFace/`` as follows(images can be downloaded from WiderFace website directly):
```Shell
data/retinaface/
train/
images/
label.txt
val/
images/
label.txt
test/
images/
label.txt
```
## Testing
Please check ``test.py`` for model usage.
Pretrained Model: [RetinaFace-R50](https://pan.baidu.com/s/1C6nKq122gJxRhb37vK0_LQ) is a medium size model with ResNet50 backbone. WiderFace validation mAP: Easy 96.5, Medium 95.6, Hard 90.4. It can output face bounding boxes and five landmarks in a single forward pass.
Light weight and large models will be released soon.
![example1](https://github.com/deepinsight/insightface/blob/master/resources/retina_R50_ex1.jpg)
![example2](https://github.com/deepinsight/insightface/blob/master/resources/retina_R50_ex2.jpg)