osmr / facedetver

Face Detection Verifier

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Face Detection Verifier

This repo contains scripts for solving the binary classification problem, where the positive class is undistorted images of a person’s face, and the negative class is everything else, including images of parts of a person’s face, face drawings, etc.

The repository contains scripts for building and analyzing the corresponding dataset, for training and testing models. Two deep learning frameworks are supported: MXNet/Gluon and PyTorch. All scripts are completely duplicated. In addition, the releases contain two training datasets and four models that solve the problem.

Deployment Instructions

Recommended repository deployment protocol on the machine with CUDA 10.0 and cuDNN 7:

  1. Install prerequesities for MXNet:
    apt update
    apt upgrade
    apt install -y htop mc wget unzip python3-pip ipython3
    apt install -y build-essential git ninja-build ccache
    apt install -y apt-transport-https build-essential ca-certificates curl git libatlas-base-dev libcurl4-openssl-dev libjemalloc-dev libhdf5-dev liblapack-dev libopenblas-dev libopencv-dev libturbojpeg libzmq3-dev ninja-build software-properties-common sudo vim-nox
    apt install -y libopenblas-dev libopencv-dev
    apt install -y libsm6 libxext6 libxrender-dev
  2. Install pip-packages from requirements.txt:
    pip3 install --upgrade numpy opencv-python imgaug tqdm
    pip3 install --upgrade mxnet-cu100 gluoncv2
    pip3 install --upgrade torch torchvision pytorchcv
  3. Clone the repo:
    mkdir projects
    cd projects
    git clone https://github.com/osmr/facedetver.git
  4. Create directory for dataset and models:
    mkdir facedetver_data
    cd facedetver_data
  5. Download and extract dataset FDV1:
    mkdir fdv1
    cd fdv1
    wget https://github.com/osmr/facedetver/releases/download/v0.0.1/fdv1_test.zip
    wget https://github.com/osmr/facedetver/releases/download/v0.0.1/fdv1_train.zip
    wget https://github.com/osmr/facedetver/releases/download/v0.0.1/fdv1_val.zip
    unzip fdv1_test.zip
    unzip fdv1_train.zip
    unzip fdv1_val.zip
  6. Or download and extract dataset FDV2:
    mkdir fdv2
    cd fdv2
    wget https://github.com/osmr/facedetver/releases/download/v0.0.2/fdv2_test.zip
    wget https://github.com/osmr/facedetver/releases/download/v0.0.2/fdv2_train.zip
    wget https://github.com/osmr/facedetver/releases/download/v0.0.2/fdv2_val.zip
    unzip fdv2_test.zip
    unzip fdv2_train.zip
    unzip fdv2_val.zip
  7. Download and extract a model:
    cd ..
    mkdir resnet18_fdv1-0014
    cd resnet18_fdv1-0014
    wget https://github.com/osmr/facedetver/releases/download/v0.0.3/resnet18_fdv1-0014-a03f116e.params.zip
    unzip resnet18_fdv1-0014-a03f116e.params.zip
  8. Run a testing script:
    cd ../../facedetver
    python3 eval_gl.py --num-gpus=1 --model=resnet18 --save-dir=../facedetver_data/resnet18_fdv1-0014/ --batch-size=100 -j=4 --resume=../facedetver_data/resnet18_fdv1-0014/resnet18_fdv1-0014-a03f116e.params --calc-flops --show-bad-samples --data-subset=test

Pretrainded Models

Model Dataset Framework Acc F1 MCC Params FLOPs/2 Remarks
ResNet-18 FDV1 Gluon 0.9976 0.9976 0.9952 11,177,538 1,819.90M Training (log)
ResNet-18 FDV1 PyTorch 0.9976 0.9976 0.9952 11,177,538 1,819.90M Training (log)
ResNet-18 FDV2 Gluon 0.9971 0.9971 0.9942 11,177,538 1,819.90M Training (log)
ResNet-18 FDV2 PyTorch 0.9971 0.9971 0.9942 11,177,538 1,819.90M Training (log)
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Face Detection Verifier

License:MIT License


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