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