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Face Parsing [ hard ROI context ]

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# Face Parsing

Implementation of a one-stage Face Parsing model using Unet architecture. 
The face is divided into 10 classes (background, eyes, nose, lips, ear, hair, teeth, eyebrows, general face, beard). 
This results in regions of interest that are difficult to segment. We address this problem by implementing different loss functions:

-  Tversky focal loss
-  Weighted Cross entropy
-  α-balanced focal loss

The latter leads to a better accuracy of 0.90 (IOU-train) and 0.72 (IOU-test) for the non fine-tuned model, with a Unet-16 driven on 1 gpu.

## Implementation

Language version : Python 3.7.6 
Operating System : MacOS Catalina 10.15.4 
Framework: Pytorch

## Results

We can observe the probability map for each channel. 
It allows us to estimate how much a pixel belongs to a specific part of the face. 
<img src="https://dl.dropboxusercontent.com/s/z6loxo2ttt9hnbl/heatmap.png?dl=0" height="300">

Segmentation sample: 
<img src="https://dl.dropboxusercontent.com/s/gk0l5dpw4k4txqc/inference.png?dl=0" height="300">

### Getting Started
Install dependencies: 
```python
# use a virtual-env to keep your packages unchanged
>> pip install -r requirements.txt
```
Train the model: 
```python
# Train the model
>> ./main.py
```
Inference: 
```python
from src.model import Unet
from src.framework import Context

img = "./img_relative_path.png"

# load model
model = torch.load("my_model.pt", map_location=torch.device('cpu'))
# create context
ctx = Context()
# predict
yhat = ctx.predict(model, img, plot=True)
```

## References

- [Mut1ny](https://www.mut1ny.com/face-headsegmentation-dataset) Face/head dataset
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf)
Olaf Ronneberger, Philipp Fischer, and Thomas Brox.
Tech report, arXiv, May 2015.
- [A novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://arxiv.org/pdf/1810.07842.pdf)
Abraham, Nabila and Khan, Naimul Mefraz.
Tech report, arXiv, Oct 2018.
- [Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf)
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár.
Tech report, arXiv, Feb 2018.

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