yangfly / sfd.gluoncv

Description

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

Environment

• mxnet 1.3.0
• gluon-cv 0.4.0
• MobulaOP required to build custom c++ operator

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 with 4 gpus

   $$ python sfd/train.py
   

   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/eval.py --model models/vgg16/sfd_best.params