Apply an automatic portrait segmentation model (aka image matting) to celebrity face dataset.
To download dataset visit above link or
cd src/data
python make_dataset.py
./models/get_pretrained.sh
or
Google Drive Link : https://drive.google.com/drive/folders/1cF8D21yGyqogenu3DCY5fZLpAnuYvtlq?usp=sharing
DeepLabv3 model has been used here, with input of 224x224x3 and output 224x224x1 mask.
Notebooks folder contains a basic and in-depth EDA notebooks, as well as model inspection(model_select.ipynb).
notebooks/demo.ipynb -> contains an implementation of the pretrained model.
notebooks/model_performance.ipynb -> Gives an overview of the model training loss and validation metrics stats.
Run main.py script.
To get argument details : python src/main.py -h
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ ├── interim <- Partially processed data.
│ └── raw <- The original, immutable data dump.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
| |
| ├── raw_data_eda.ipynb <- Basic EDA, on raw dataset size, image size, format etc.
| |
| ├── advanced_eda.ipynb <- Advanced EDA performed on masks and mask averages.
| |
| ├── model_select.ipynb <- Possible model architectures and known metrics.
| |
| ├── model_performance.ipynb <- Performance metrics of refitted, pretrained model.
| |
| └── demo.ipynb <- Implementation of pretrained model, post training, on sample image.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment
│
├── src <- Source code for use in this project.
│ │
│ ├── data <- Scripts to download or generate data
│ │
│ ├── utils <- Scripts utilities used during data generation or training
│ │
│ └── training <- Scripts to train models
- Model architecture based on DeepLabv3
- Dataset from https://github.com/switchablenorms/CelebAMask-HQ
- This project repo was based on cookiecutter template -> https://github.com/tdeboissiere/cookiecutter-deeplearning
src/training/trainer.pymodule based on https://github.com/msminhas93/DeepLabv3FineTuning/blob/master/trainer.py
All code in the repo is under MIT license, external code and data are under respective licenses.
[](https://opensource.org/licenses/MIT)
