https://github.com/deepinx/iqiyi-vid-challenge

Code for IQIYI-VID(IQIYI Video Person Identification) Challenge Implemented in Python and MXNet
https://github.com/deepinx/iqiyi-vid-challenge

face-alignment face-detection face-recognition mxnet

Last synced: 2 months ago
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Code for IQIYI-VID(IQIYI Video Person Identification) Challenge Implemented in Python and MXNet

• Host: GitHub
• URL: https://github.com/deepinx/iqiyi-vid-challenge
• Owner: deepinx
• Created: 2019-02-18T03:45:55.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-22T01:55:39.000Z (over 5 years ago)
• Last Synced: 2024-08-01T22:40:58.017Z (5 months ago)
• Topics: face-alignment, face-detection, face-recognition, mxnet
• Language: Python
• Homepage:
• Size: 592 KB
• Stars: 47
• Watchers: 4
• Forks: 13
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-MXNet - iqiyi-vid-challenge 1st code

README

        
## Code for IQIYI-VID Challenge based on ESSH and Insightface

Recently [www.iqiyi.com](http://www.iqiyi.com) released a great video person dataset called [IQIYI_VID](http://challenge.ai.iqiyi.com/detail?raceId=5afc36639689443e8f815f9e) 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](https://github.com/deepinx/enhanced-ssh-mxnet) is applied for face detection and alignment. [Insightface](https://github.com/deepinsight/insightface) scheme is used for face recognition.

Pre-trained models can be downloaded on [BaiduCloud](https://pan.baidu.com/s/1vS_Uv9l5XZLeMwXPs_XzOA) or [GoogleDrive](https://drive.google.com/open?id=1_8-GcZbYNGvm_msyuwqDw4u9mGnHqSQP).

## Environment

This repository has been tested under the following environment:

-   Python 2.7 

-   Ubuntu 18.04

-   Mxnet-cu90 (==1.3.0)

## Installation

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](http://challenge.ai.iqiyi.com/detail?raceId=5afc36639689443e8f815f9e) and unzip them to `data/iqiyi_vid` directory. 

## Usage

1.  Detect faces on train+val dataset and test dataset respectively using ESSH model.  Model `model-r50-gg` is used to judge the quality of the detected faces.

```

python detect.py --model ./model/model-r50-gg/model,0 --output ./output/det_trainval --dataset ./data/iqiyi_vid --gpu 0 --stage trainval

python detect.py --model ./model/model-r50-gg/model,0 --output ./output/det_test --dataset ./data/iqiyi_vid --gpu 0 --stage test

```

2. Extract features to the detected faces of train+val and test dataset respectively using `model-r100-gg` model.

```

python feature.py --model ./model/model-r100-gg/model,0 --input ./output/det_trainval --output ./output/feat_trainval  --gpu 0

python feature.py --model ./model/model-r100-gg/model,0 --input ./output/det_test --output ./output/feat_test --gpu 0

```

3.  Re-save the extracted face features for training the MLP network.

```

python genfeat.py --inputs ./output/feat_trainval --output ./output/trainval

```

4. Train the MLP network for face ID recognition using train+val datasets.

```

python train_mlp.py --data ./output/trainval --prefix ./model/iqiyi --ckpt 1 --network r50 --lr 0.2 --per-batch-size 1024

```

5.  Predict face ID from features of the test dataset using the pre-trained MLP network.

```

python predict.py --model ./model/iqiyi,40 --gpu 0 --inputs ./output/feat_test --output ./output/pred_test

```

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

## License

MIT LICENSE

## Reference

```

@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},

}

```

## Acknowledgment

The code is adapted based on an intial fork from the [insightface](https://github.com/deepinsight/insightface) repository.