Face occlusion classification

git clone https://github.com/LamKser/face-occlusion-classification.git
cd face-occlusion-classification

:computer: Hardware & Environment

• All the train and test processes are done on google colab with GPU Tesla T4

  conda env create -f environment.yml
  conda activate face-occlusion
  

:books: Dataset

• Crawl 9,749 images from the internet, crop the face by using FaceMaskDetection and divide into 2 classes:
  • 0 - Non-occluded face
  • 1 - Occluded face

Figure 1: Non-occluded face example

Figure 2: Occluded face example

• Then split the dataset into 3 sets (7 - 2 - 1):

  • Train set : 6,826 images
  • Val set : 1,945 images
  • Test set : 978 images

• Data structure:

  face_occlusion
  ├───Train
  │   ├───1
  │   │   ├─face_0.jpg
  │   │   ├─face_1.jpg
  │   │   └...
  │   └───0
  ├───Val
  │   ├───1
  │   └───0
  └───Test
      ├───1
      └───0
  

• :link: Data link: face occlusion dataset

:triangular_ruler: Config

• To use other model or change hyperparameters, you can edit train.yml and test.yml in configs folder
• Available models: densenet169, resnet18, resnet50

:building_construction: Train model

• Train

  python train.py --opt configs/train.yml
  

• Show the training and validation progress

  tensorboard --logdir logger
  

• If using wandb to log training process:

  wandb:
      project: <Type your project>
      name: <Type experiment name>
  

:chart_with_upwards_trend: Test model

• Test the model

  python test_model.py --opt configs/test.yml
  

• Test single image

  python test_single_image.py --model <model_name> --weight <weight_path> --image <image_path>
  

• ONNX model

  • Convert pytorch model to onnx

    python onnx/convert_2_onnx.py --model <model name> \\
                                  --weight <weight and checkpoint file> \\
                                  --save <path/to/save/onnx/*.onnx> \\
                                  --opset_version <version> (optional)
    

  • Run onnx model

    python onnx/run_onnx.py --onnx <onnx file> --img <your image>
    

:bar_chart: Results (Train/Val/Test)

• All the trained model: trained model
• The pretrained models are trained with 30 epochs

Last model