Metadata-Version: 2.1
Name: selfclean
Version: 0.0.2
Summary: A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.
Home-page: https://github.com/Digital-Dermatology/SelfClean
Author: Fabian Groeger
Author-email: fabian.groeger@unibas.ch
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: tqdm
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: wget ==3.2
Requires-Dist: pytest
Requires-Dist: torchvision
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Requires-Dist: black >=22.6
Requires-Dist: coverage >=6
Requires-Dist: darglint >=1.8
Requires-Dist: isort >=5.10
Requires-Dist: pipreqs ==0.4.11
Requires-Dist: pre-commit >=2.20
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# SelfClean
A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.

<p align="center">
  <img src="assets/SelfClean_Teaser.svg">
</p>

[**SelfClean Paper**](https://arxiv.org/abs/2305.17048)


## Development Environment
Run `make` for a list of possible targets.

### Installation
Run these commands to install the project:
```bash
make init
make install
```

To run linters on all files:
```bash
pre-commit run --all-files
```

### Code and test conventions
- `black` for code style
- `isort` for import sorting
- `pytest` for running tests


