amigocdt / iqiyi-vid-challenge Goto Github PK

Recently www.iqiyi.com released a great video person dataset called IQIYI_VID and also launched a person search competition on it. It is a very large and real dataset worth trying to verify your face model accuracy precisely.

This repository contains the code for IQIYI-VID(IQIYI video person identification) Challenge. The methods are implemented in Python and MXNet. The Enhanced SSH (ESSH) from enhanced-ssh-mxnet is applied for face detection and alignment. Insightface scheme is used for face recognition.

Pre-trained models can be downloaded on BaiduCloud or GoogleDrive.

This repository has been tested under the following environment:

• Python 2.7
• Ubuntu 18.04
• Mxnet-cu90 (==1.3.0)

1. Prepare the environment.

2. Clone the repository.

3. Type make to build necessary cxx libs.

4. Download the pre-trained model and place it in ./model/

5. Download the IQIYI-VID Datasets from IQIYI_VID and unzip them to your disk.

1. Use python detect.py to train+val dataset and test dataset respectively for face detection.

2. Use python feature.py to detections of train+val and test dataset respectively for feature extraction.

3. Use python genfeat.py to re-save the extracted face features for training the MLP network.

4. Run train_mlp.py to train the MLP network for face ID recognition using train+val datasets.

5. Run python predict.py to features of test dataset for predicting face ID using the trained MLP network.

6. Run python submit.py to output the final submissions for IQIYI-VID Challenge.

MIT LICENSE

@article{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
journal={arXiv:1801.07698},
year={2018}
}

@inproceedings{Najibi2017SSH,
  title={SSH: Single Stage Headless Face Detector},
  author={Najibi, Mahyar and Samangouei, Pouya and Chellappa, Rama and Davis, Larry S.},
  booktitle={IEEE International Conference on Computer Vision},
  year={2017},
}

The code is adapted based on an intial fork from the insightface repository.