Deblina Bhattacharjee, Seungryong Kim, Guillaume Vizier, Mathieu Salzmann
CVPR 2020 Paper
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── docker <- Dockerfiles for running the models
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── app.py <- Interactive demonstration of the behavior of the multi-box losses
- Clone the repo: `git clone
- Install the requirements:
pip install -r requirements.txt
- Run
python app.py - Open
localhost:8050on your favorite browser.
If you find the code, data, or the models useful, please cite this paper:
@InProceedings{Bhattacharjee_2020_CVPR,
author = {Bhattacharjee, Deblina and Kim, Seungryong and Vizier, Guillaume and Salzmann, Mathieu},
title = {DUNIT: Detection-Based Unsupervised Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
[Creative Commons Attribution Non-commercial No Derivatives](http://creativecommons.org/licenses/by-nc-nd/3.0/)
Project based on the cookiecutter data science project template. #cookiecutterdatascience