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StanislasBertrand
172 Stars 11 Forks MIT License 36 Commits 1 Opened issues

Description

RetinaFace (RetinaFace: Single-stage Dense Face Localisation in the Wild, published in 2019) reimplemented in Tensorflow 2.0, with pretrained weights available !

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# 175,699
Keras
C
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insight...
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RetinaFace-tf2

RetinaFace (RetinaFace: Single-stage Dense Face Localisation in the Wild, published in 2019) reimplemented in Tensorflow 2.0, with pretrained weights available. Resnet50 backbone.

Original paper -> arXiv
Original Mxnet implementation -> Insightface

Table of contents

  1. Installation
  2. Usage
  3. Benchmark
  4. Evaluation
  5. Acknowledgements

example output : testing on a random internet selfie


INSTALLATION

To install dependencies, if you have a GPU, run :

pip install -r requirements-gpu.txt
If not, run :
pip install -r requirements.txt
Then build the rcnn module by running :
make

USAGE

Download pretrained weights on Dropbox and save them in the data folder
Run :

angular2
python detect.py --weights_path="./data/retinafaceweights.npy" --sample_img="./sample-images/WC_FR.jpeg"
Python usage : ```python from retinaface import RetinaFace import cv2

detector = RetinaFace("./data/retinafaceweights.npy", False, 0.4) img = cv2.imread("./sample-images/WC_FR.jpeg") faces, landmarks = detector.detect(img, 0.9) ```

BENCHMARK

mAP result values on the WIDERFACE validation dataset:

| Model | Easy | Medium | Hard | |---|---|---|---| |Original Mxnet implementation | 96.5 | 95.6 | 90.4 | | Ours | 95.6 | 94.6 | 88.5 |

EVALUATE ON WIDERFACE

In order to verify the models accuracy on the WiderFace dataset: * Run the model on the dataset and generate text files as results

angular2
python eval_widerface --weights_path="data/retinafaceweights.npy" --widerface_data_dir = "/data/WIDER_test/images" --save_folder="./WiderFace-Evaluation/results/"
* Evaluate the results
angular2
cd ./WiderFace-Evaluation
python setup.py build_ext --inplace
python evaluation.py -p ./results_val/ -g ./ground_truth/

ACKNOWLEDGEMENTS

This work is largely based on the original implementation by the amazing insightface team
Evaluation on widerface done with the Widerface-Evaluation repo
If you use this repo, please reference the original work :

@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}
}

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