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sfd.gluoncv's Introduction

Description

This work is to reproduce S³FD, a real-time Single Shot Scale-invariant Face Detector.

Environment

Preparation

# install MobulaOP following: https://github.com/wkcn/MobulaOP
$$ git clone https://github.com/yangfly/sfd.gluoncv.git
$$ cd sfd.gluoncv

Training your own model

  1. download wider face dataset into widerface/downloads/
    $$ tree widerface/downloads
    widerface/downloads
    ├── eval_tools.zip
    ├── Submission_example.zip
    ├── wider_face_split.zip
    ├── WIDER_test.zip
    ├── WIDER_train.zip
    └── WIDER_val.zip
    
  2. Parpare data: unzip data, annotations and eval_tools
    $$ python tool/parpare.py
    $$ tree widerface -L 1
    widerface
    ├── downloads
    ├── eval_tools
    ├── wider_face_split
    ├── WIDER_train
    └── WIDER_val
    
  3. Prepare custom val dataset for quick validation (crop and resize to 640)
    $$ python tool/build_custom_val.py
    $$ tree widerface -L 1
    widerface
    ├── downloads
    ├── eval_tools
    ├── WIDER_custom
    ├── wider_face_split
    ├── WIDER_train
    └── WIDER_val
    
  4. train vgg16 based sfd
    $$ python sfd/train.py --network vgg16 --save-prefix models/vgg16/
    
    more supported base models in sfd/nn/sfd.py
  5. demo
    $$ python sfd/demo.py --model models/vgg16/sfd_best.params
    
  6. eval on WIDER_val
    $$ python sfd/val.py --model models/vgg16/sfd_best.params
    

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