exp2: Face Detection Based on Neural Network

2020 PostGraduate

You can click here to get the Chinese version.

Motivation

1. Understand the basic theory of face detection using neural network.
2. Understand the processes of MTCNN and use it in practice.

Dataset for Training

1. When training PNet and Rnet, we use WiderFace for face classification and face bounding box regression.
2. When training ONet, we use FacePoint for face classification and face bounding box regression, and use set Training Dataset for face feature point regression.

Environment for Experiment

anaconda3
pytorch >= 1.0.0
torchvision
opencv-python
tensorflow (optional)

Experiment Steps

1. get the codes

The complete codes for this experiment is given in MTCNN_pytorch, you can download it directly or using git clone command.

git clone https://github.com/xiezheng-cs/mtcnn_pytorch.git

2. install the environment
1. download anaconda3 for your computer；
2. use pip or conda to install pytorch 1.0.0 and torchvision；
3. use pip or conda to install opencv-python;

conda install pytorch=1.0
conda install torchvision
pip install opencv-python

3. test the given models
Use the model we have trained before to run on the test dataset, which is under mtcnn_pytorch/data/test_images/ and contains 64 pictures, input the command below and then go to mtcnn_pytorch/data/test_images/ to examine the result.

cd mtcnn_pytorch/
python test_image.py

4. training (optional)
NOTE： You should check whether the path of the training dataset is consistent with the path in the code. If not, you must modify the path in the code before you start training.

1. train PNet (optional)

   cd mtcnn_experiment
   python preprocessing/gen_pnet_data.py
   python preprocessing/assemble_pnet_imglist.py
   python training/pnet/train.py

2. train RNet (optional)

   cd mtcnn_experiment
   python preprocessing/gen_rnet_data.py
   python preprocessing/assemble_rnet_imglist.py
   python training/rnet/train.py

3. train ONet (optional)

   cd mtcnn_experiment
   python preprocessing/gen_landmark_48.py
   python preprocessing/gen_onet_data.py
   python preprocessing/assemble_onet_imglist.py
   python training/onet/train.py

   After finishing training, you can find three trained models in mtcnn_pytorch/results/.

4. test your model (optional)
   Use the given test dataset, which is in mtcnn_pytorch/data/test_images/ and contains 64 pictures, to test your model. Run the commands below and then get the results in mtcnn_pytorch/data/you_result/.

   cd mtcnn_experiment/
   python test_youModel_images.py

   NOTE: You should check whether the path of the training dataset is consistent with the path in the code. If not, you must modify the path in the code before you start training.

Finishing experiment report according to experiment result. The template of report can be found in here.

Submission

Requirement for Submission

• You only have to submit the experiment report. The experiment codes is not necessary for the submission.
• The submission of experiment report should be in PDF format（The template of report may be not very fit for the experiments, please revise it by yourself）
• please send all experiment reports to teaching assistant (jiaju.wu@qq.com, the email title should contain your name and your student number)

Deadline

• 24:00 on Jan. 7th, 2021，please send all experiment reports to teaching assistant (jiaju.wu@qq.com) before deadline.

P.S.

• The reports can be written in Chinese or English, in LaTeX or Word（If you write reports in Word, you need to export them to PDF format.）