torch_Retina_face_bifpn_bce_try_0124_atss.rar

# RetinaFace in PyTorch A [PyTorch](https://pytorch.org/) implementation of [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641). Model size only 1.7M, when Retinaface use mobilenet0.25 as backbone net. We also provide resnet50 as backbone net to get better result. The official code in Mxnet can be found [here](https://github.com/deepinsight/insightface/tree/master/RetinaFace). ## WiderFace Val Performance in single scale When using Resnet50 as backbone net. | Style | easy | medium | hard | |:-|:-:|:-:|:-:| | Pytorch (same parameter with Mxnet) | 94.82 % | 93.84% | 89.60% | | Pytorch (original image scale) | 95.48% | 94.04% | 84.43% | | Mxnet | 94.86% | 93.87% | 88.33% | | Mxnet(original image scale) | 94.97% | 93.89% | 82.27% | ## WiderFace Val Performance in single scale When using Mobilenet0.25 as backbone net. | Style | easy | medium | hard | |:-|:-:|:-:|:-:| | Pytorch (same parameter with Mxnet) | 88.67% | 87.09% | 80.99% | | Pytorch (original image scale) | 90.70% | 88.16% | 73.82% | | Mxnet | 88.72% | 86.97% | 79.19% | | Mxnet(original image scale) | 89.58% | 87.11% | 69.12% | <p align="center"><img src="curve/Widerface.jpg" width="640"\></p> ## FDDB Performance. | FDDB(pytorch) | performance | |:-|:-:| | Mobilenet0.25 | 98.64% | | Resnet50 | 99.22% | <p align="center"><img src="curve/FDDB.png" width="640"\></p> ### Contents - [Installation](#installation) - [Training](#training) - [Evaluation](#evaluation) - [References](#references) ## Installation ##### Clone and install 1. git clone https://github.com/biubug6/Pytorch_Retinaface.git 2. Pytorch version 1.1.0+ and torchvision 0.3.0+ are needed. 3. Codes are based on Python 3 ##### Data 1. Download the [WIDERFACE](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) dataset. 2. Download annotations (face bounding boxes & five facial landmarks) from [baidu cloud](https://pan.baidu.com/s/1Laby0EctfuJGgGMgRRgykA) or [dropbox](https://www.dropbox.com/s/7j70r3eeepe4r2g/retinaface_gt_v1.1.zip?dl=0) 3. Organise the dataset directory as follows: ```Shell ./data/widerface/ train/ images/ label.txt val/ images/ wider_val.txt ``` ps: wider_val.txt only include val file names but not label information. ##### Data1 We also provide the organized dataset we used as in the above directory structure. Link: from [google cloud](https://drive.google.com/open?id=11UGV3nbVv1x9IC--_tK3Uxf7hA6rlbsS) or [baidu cloud](https://pan.baidu.com/s/1jIp9t30oYivrAvrgUgIoLQ) Password: ruck ## Training We provide restnet50 and mobilenet0.25 as backbone network to train model. We trained Mobilenet0.25 on imagenet dataset and get 46.58% in top 1. If you do not wish to train the model, we also provide trained model. Pretrain model and trained model are put in [google cloud](https://drive.google.com/open?id=1oZRSG0ZegbVkVwUd8wUIQx8W7yfZ_ki1) and [baidu cloud](https://pan.baidu.com/s/12h97Fy1RYuqMMIV-RpzdPg) Password: fstq . The model could be put as follows: ```Shell ./weights/ mobilenet0.25_Final.pth mobilenetV1X0.25_pretrain.tar Resnet50_Final.pth ``` 1. Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in ``data/config.py and train.py``. 2. Train the model using WIDER FACE: ```Shell CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --network resnet50 or CUDA_VISIBLE_DEVICES=0 python train.py --network mobile0.25 ``` ## Evaluation ### Evaluation widerface val 1. Generate txt file ```Shell python test_widerface.py --trained_model weight_file --network mobile0.25 or resnet50 ``` 2. Evaluate txt results. Demo come from [Here](https://github.com/wondervictor/WiderFace-Evaluation) ```Shell cd ./widerface_evaluate python setup.py build_ext --inplace python evaluation.py ``` 3. You can also use widerface official Matlab evaluate demo in [Here](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html) ### Evaluation FDDB 1. Download the images [FDDB](https://drive.google.com/open?id=17t4WULUDgZgiSy5kpCax4aooyPaz3GQH) to: ```Shell ./data/FDDB/images/ ``` 2. Evaluate the trained model using: ```Shell python test_fddb.py --trained_model weight_file --network mobile0.25 or resnet50 ``` 3. Download [eval_tool](https://bitbucket.org/marcopede/face-eval) to evaluate the performance. <p align="center"><img src="curve/1.jpg" width="640"\></p> ## References - [FaceBoxes](https://github.com/zisianw/FaceBoxes.PyTorch) - [Retinaface (mxnet)](https://github.com/deepinsight/insightface/tree/master/RetinaFace) ``` @inproceedings{deng2019retinaface, title={RetinaFace: Single-stage Dense Face Localisation in the Wild}, author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos}, booktitle={arxiv}, year={2019} ```