# Pytorch_Face Detector **Repository Path**: Supernova_KS/Pytorch-Face-Detector ## Basic Information - **Project Name**: Pytorch_Face Detector - **Description**: GitHub网友:yxlijun的代码:Dual Shot Face Detector 人脸检测的一个PyTorch实现 - **Primary Language**: Python - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-07-19 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## DSFD: Dual Shot Face Detector ## [A PyTorch Implementation of Dual Shot Face Detector](https://arxiv.org/abs/1810.10220?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+arxiv%2FQSXk+%28ExcitingAds%21+cs+updates+on+arXiv.org%29) ### Description I use basenet [vgg](https://pan.baidu.com/s/1Q-YqoxJyqvln6KTcIck1tQ) to train DSFD,the model can be downloaded in [DSFD](https://pan.baidu.com/s/17cpDHEwYVxWmOIPqUy5zCQ).the AP in WIDER FACE as following: | Easy MAP | Medium MAP | hard MAP | | ---------|------------| --------- | | 0.946 | 0.937 | 0.880 | the AP in AFW,PASCAL,FDDB as following: | AFW | PASCAL | FDDB | | --------- |-----------| ---------| | 99.89 | 99.11 | 0.983 | I'm using resnet50/resnet101 to train DSFD,the result will be published later on ### 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 --model vgg\resnet50\resnet101 --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 1. demo
demo demo
### References * [Dual Shot Face Detector](https://arxiv.org/abs/1810.10220?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+arxiv%2FQSXk+%28ExcitingAds%21+cs+updates+on+arXiv.org%29) * [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch)