Deep Head Pose 2 - Keras Tensorflow 2

I. Introduction

• This repository contains an implementation of Deep Head Pose based on original code from Oreobird/tf-keras-deep-head-pose with following changes:

  • Use ShuffleNetV2 or EfficientNet-B0 as backbone network. The ideal of using ShuffleNetV2 comes from this Pytorch implementation.
  • Rewrite data generator for multi-threading.
  • Add 5-point landmark as an output branch.
  • Support 3 datasets: BIWI, AFLW2000, 300W_LP.
    • All datasets are converted to uniform format (png files for images and JSON files for labels).
    • Use Retina Face for generating bounding box and landmark label automatically for BIWI dataset.
    • Re-label landmark points for AFLW2000. The final dataset can be downloaded from here.
  • Write image augmentation code using multiple methods: blur, crop, add padding, sharpen, dropout, flip, adjust color and constrast...

• The original Pytorch version: https://github.com/natanielruiz/deep-head-pose

• Pretrained model for:

  • ShuffleNet V2: models/shufflenet_v2.h5.
  • EfficientNet: https://1drv.ms/u/s!Av71xxzl6mYZgddGNOolzhRWvELnlQ?e=TCd055.

I. Environment:

• We use Tensorflow 2.0.0.
• Install environment from environment.yml file using Anaconda and Miniconda and activate that environment.

II. Dataset preparation

• 1. Download 300W_LP, BIWI and AFLW2000 datasets and put into data/300W_LP, data/BIWI, data/AFLW2000 respectively.
• 2. Use following scripts to generate data from those datasets: make_data_300W_LP.py, make_data_BIWI.py, make_data_AFLW2000.py. You can download final AFLW2000 from here.
• 3. Run sh split_data_set.sh to split dataset. This script will take 2000 images from 300W_LP and 2000 images from BIWI dataset to build validation set at data/val_set and remaining parts of these dataset to build training set at data/train_set. We use AFLW2000 as testing set.

III. Training and testing

Training

• Modify config files at config_shufflenetv2.json and config_efficientnet.json to use for training and testing.

• Example of commands for training:

python train.py -c config_shufflenetv2.json

Or

python train.py -c config_efficientnet.json

Testing

python test.py -c config_shufflenetv2.json

Or

python test.py -c config_efficientnet.json