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Real-Time Face Detector

This repository contains training scripts for lightweight SSD-based face detector. The detector is based on the MobileNetV2 backbone and has a single SSD head with manually designed anchors. As a result, its computational complexity is 0.51 GMACs and it has 1.03 M of parameters.

Prerequisites

  1. Download the WIDER Face and unpack it to the data folder.

  2. Annotation in the Pascal Visual Objects in Context (Pascal VOC) format can be found in this repository. Move the annotation files from the WIDER_train_annotations and WIDER_val_annotations folders to the Annotation folders inside the corresponding directories WIDER_train and WIDER_val. Also, copy the annotation lists val.txt and train.txt to data/WIDERFace from WIDER_train_annotations and WIDER_val_annotations. The directory should be like this:

    data
└── WIDERFace
    ├── WIDER_train
    │   ├──0--Parade
    │   ├── ...
    │   └── Annotations
    ├── WIDER_val
    │   ├──0--Parade
    │   ├── ...
    │   └── Annotations
    ├── val.txt
    └── train.txt

Training

  1. Download pretrained MobileNetV2 weights mobilenet_v2.pth.tar. Move the file with weights to the folder snapshots. Or use the checkpoint that was trained on Wider.
  2. To train the detector on a single GPU, run in your terminal: 
    python3 ../../external/mmdetection/tools/train.py \
configs/mobilenetv2_tiny_ssd300_wider_face.py

Validation

  1. To dump detection of your model:

    python3 ../../external/mmdetection/tools/test.py    \
  configs/mobilenetv2_tiny_ssd300_wider_face.py   \
  <CHECKPOINT>   \
  --out result.pkl

  2. Then run the following:

    python3 ../../external/mmdetection/tools/voc_eval.py    \
  result.pkl    \
  configs/mobilenetv2_tiny_ssd300_wider_face.py

Observe 0.305 AP on the validation set. For more detailed results and comparison with vanilla SSD300, see ../../external/mmdetection/configs/wider_face/README.md.

Conversion to the OpenVINO™ format

  1. Convert PyTorch* model to the ONNX* format by running the script:

    python3 tools/onnx_export.py \
      configs/mobilenetv2_tiny_ssd300_wider_face.py
      <CHECKPOINT> \
      face_detector.onnx

  2. Convert ONNX model to the OpenVINO™ format with the Model Optimizer with the command below:

    mo.py --input_model face_detector.onnx \
  --scale 255 \
  --reverse_input_channels \
  --output_dir=./IR \
  --data_type=FP32

This produces model face_detector.xml and weights face_detector.bin in single-precision floating-point format (FP32). The obtained model expects normalized image in planar BGR format.

Python Demo

To run the demo, connect a webcam end execute the command:

python3 tools/detection_live_demo.py  \
  configs/mobilenetv2_tiny_ssd300_wider_face.py \
  <CHECKPOINT> \
  --cam_id 0

Estimate Theoretical Computational Complexity

To get per-layer computational complexity estimations, run the following command:

python3 tools/count_flops.py configs/mobilenetv2_tiny_ssd300_wider_face.py

Fine-Tuning

  • Dataset should have the same data layout as WIDER Face in the Pascal VOC format described in this instruction.
  • Fine-tuning steps are the same as the step 2 for training, but some adjustments in config are needed:
    • specify initial checkpoint containing a valid detector in load_from field of config configs/mobilenetv2_tiny_ssd300_wider_face.py
    • edit the data section of config to pass a custom dataset.