Adaptive-Wing-Loss-for-Robust-Face-Alignment-via-Heatmap-Regression
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Adaptive-Wing-Loss-for-Robust-Face-Alignment-via-Heatmap-Regression
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.
📝 TODO
- [x] 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
- download [part1] ~ [part2]
- 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
more detail about 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 |