mirror of
https://github.com/deepinsight/insightface.git
synced 2026-05-14 12:17:55 +00:00
85 lines
3.5 KiB
Markdown
85 lines
3.5 KiB
Markdown
# RetinaFace Face Detector
|
|
|
|
## Introduction
|
|
|
|
RetinaFace is a practical single-stage [SOTA](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) face detector which is initially introduced in [arXiv technical report](https://arxiv.org/abs/1905.00641) and then accepted by [CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_RetinaFace_Single-Shot_Multi-Level_Face_Localisation_in_the_Wild_CVPR_2020_paper.html).
|
|
|
|
|
|
|
|
|
|
|
|
## Data
|
|
|
|
1. Download our 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)
|
|
|
|
2. Download the [WIDERFACE](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) dataset.
|
|
|
|
3. Organise the dataset directory under ``insightface/RetinaFace/`` as follows:
|
|
|
|
```Shell
|
|
data/retinaface/
|
|
train/
|
|
images/
|
|
label.txt
|
|
val/
|
|
images/
|
|
label.txt
|
|
test/
|
|
images/
|
|
label.txt
|
|
```
|
|
|
|
## Install
|
|
|
|
1. Install MXNet with GPU support.
|
|
2. Install Deformable Convolution V2 operator from [Deformable-ConvNets](https://github.com/msracver/Deformable-ConvNets) if you use the DCN based backbone.
|
|
3. Type ``make`` to build cxx tools.
|
|
|
|
## Training
|
|
|
|
Please check ``train.py`` for training.
|
|
|
|
1. Copy ``rcnn/sample_config.py`` to ``rcnn/config.py``
|
|
2. Download ImageNet pretrained models and put them into ``model/``(these models are not for detection testing/inferencing but training and parameters initialization).
|
|
|
|
ImageNet ResNet50 ([baidu cloud](https://pan.baidu.com/s/1WAkU9ZA_j-OmzO-sdk9whA) and [dropbox](https://www.dropbox.com/s/48b850vmnaaasfl/imagenet-resnet-50.zip?dl=0)).
|
|
|
|
ImageNet ResNet152 ([baidu cloud](https://pan.baidu.com/s/1nzQ6CzmdKFzg8bM8ChZFQg) and [dropbox](https://www.dropbox.com/s/8ypcra4nqvm32v6/imagenet-resnet-152.zip?dl=0)).
|
|
|
|
3. Start training with ``CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --prefix ./model/retina --network resnet``.
|
|
Before training, you can check the ``resnet`` network configuration (e.g. pretrained model path, anchor setting and learning rate policy etc..) in ``rcnn/config.py``.
|
|
4. We have two predefined network settings named ``resnet``(for medium and large models) and ``mnet``(for lightweight models).
|
|
|
|
## Testing
|
|
|
|
Please check ``test.py`` for testing.
|
|
|
|
## RetinaFace Pretrained Models
|
|
|
|
Pretrained Model: RetinaFace-R50 ([baidu cloud](https://pan.baidu.com/s/1C6nKq122gJxRhb37vK0_LQ) or [dropbox](https://www.dropbox.com/s/53ftnlarhyrpkg2/retinaface-R50.zip?dl=0)) is a medium size model with ResNet50 backbone.
|
|
It can output face bounding boxes and five facial landmarks in a single forward pass.
|
|
|
|
WiderFace validation mAP: Easy 96.5, Medium 95.6, Hard 90.4.
|
|
|
|
To avoid the confliction with the WiderFace Challenge (ICCV 2019), we postpone the release time of our best model.
|
|
|
|
## Third-party Models
|
|
|
|
[yangfly](https://github.com/yangfly): RetinaFace-MobileNet0.25 ([baidu cloud](https://pan.baidu.com/s/1P1ypO7VYUbNAezdvLm2m9w):nzof).
|
|
WiderFace validation mAP: Hard 82.5. (model size: 1.68Mb)
|
|
|
|
[clancylian](https://github.com/clancylian/retinaface): C++ version
|
|
|
|
## References
|
|
|
|
```
|
|
@inproceedings{Deng2020CVPR,
|
|
title = {RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild},
|
|
author = {Deng, Jiankang and Guo, Jia and Ververas, Evangelos and Kotsia, Irene and Zafeiriou, Stefanos},
|
|
booktitle = {CVPR},
|
|
year = {2020}
|
|
}
|
|
```
|
|
|
|
|