/Adaptive_Wing_Loss_for_Robust_Face_Alignment_via_Heatmap_Regression

Adaptive-Wing-Loss-for-Robust-Face-Alignment-via-Heatmap-Regression

Primary LanguagePython

Adaptive-Wing-Loss-for-Robust-Face-Alignment-via-Heatmap-Regression

❗ongoing repo

Pytorch implementation of paper. official implementation can be found at official.

blog post about the paper(korean) can be found here.

result

📝 TODO

  • prototype
  • albumentation data augmentation
  • evalutaion on 300W + data augmentation
  • performance tuning
  • dependency check
  • provide pretrained weight
  • apply different model (such as DLA, Unet)
  • apply similar loss (such as Focal-loss)
  • apply Integral regression moduel (AWing + Integral)

Prerequisites

  • Python 3.6 +
  • Pytorch 1.1.0
  • Scipy 0.19.1
  • cv2 3.3.0

Usage

First, download dataset(Currently 300W supported).

300W link

  1. download [part1] ~ [part2]
  2. locate 300W images, pts files according to this structure

data

|-- 300W
|   |-- 01_Indoor
|   |-- 02_Ourdoor

To train a model with downloaded dataset:

$ python train.py

To test single image result:

$ python detect.py

Model overview

model

more detail about model

model

loss function design

AWing → (lossMatrix) → Loss_weighted

evalutaion

evalutaion on 300W testing dataset

evaluation result will soon be updated

method NME FR(10)
the paper 3.07 X
this repo x 0.8

Reference