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33 changes: 33 additions & 0 deletions CONTRIBUTING.md
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# Contribution Guide

We welcome any contributions whether it's,

- Submitting feedback
- Fixing bugs
- Or implementing a new feature.

Please read this guide before making any contributions.

#### Submit Feedback
The feedback should be submitted by creating an issue at [GitHub issues](https://github.com/idealo/image-dedup/issues).
Select the related template (bug report, feature request, or custom) and add the corresponding labels.

#### Fix Bugs:
You may look through the [GitHub issues](https://github.com/idealo/image-dedup/issues) for bugs.

#### Implement Features
You may look through the [GitHub issues](https://github.com/idealo/image-dedup/issues) for feature requests.

## Pull Requests (PR)
1. Fork the repository and a create a new branch from the master branch.
2. For bug fixes, add new tests and for new features please add changes to the documentation.
3. Do a PR from your new branch to our `dev` branch of the original Image Super-Resolution repo.

## Documentation
- Make sure any new function or class you introduce has proper docstrings.

## Testing
- We use [pytest](https://docs.pytest.org/en/latest/) for our testing. Make sure to write tests for any new feature and/or bug fixes.

## Main Contributor List
We maintain a list of main contributors to appreciate all the contributions.
203 changes: 203 additions & 0 deletions LICENSE
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Copyright 2019 idealo internet GmbH. All rights reserved.

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44 changes: 36 additions & 8 deletions README.md
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Finding duplicates in an image dataset is a recurring task. imagededup is a python package that provides functionality
to carry out this task effectively. The deduplication problem generally caters to 2 broad issues:
* Finding exact duplicates

<p align="center">
<img src="_readme_figures/103500.jpg" width="300" />
<img src="_readme_figures/103500.jpg" width="300" />
</p>

* Finding near duplicates

<p align="center">
<img src="_readme_figures/103500.jpg" width="300" />
<img src="_readme_figures/103501.jpg" width="300" />
</p>

Traditional methods such as hashing algorithms are particularly good at finding exact duplicates while more modern
methods involving convolutional neural networks are also adept at finding near duplicates due to their ability to
capture basic contours in images.
Expand Down Expand Up @@ -68,7 +79,7 @@ where the returned variable *duplicates* is a dictionary with the following cont
{
'image1.jpg': ['image1_duplicate1.jpg',
'image1_duplicate2.jpg'],
'image2.jpg': [],
'image2.jpg': [..],
..
}
```
Expand Down Expand Up @@ -310,12 +321,12 @@ To determine the quality of deduplication algorithm and corresponding threshold,

Given a ground truth mapping consisting of file names and a list of duplicates for each file along with a retrieved
mapping from the deduplication algorithm for the same files, the following metrics can be obtained using the framework:
- Mean Average Precision (MAP)
- Mean Normalized Discounted Cumulative Gain (NDCG)
- Jaccard Index
- Per class Precision (class 0 = non-duplicate image pairs, class 1 = duplicate image pairs)
- Per class Recall (class 0 = non-duplicate image pairs, class 1 = duplicate image pairs)
- Per class f1-score (class 0 = non-duplicate image pairs, class 1 = duplicate image pairs)
- [Mean Average Precision](https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision) (MAP)
- [Mean Normalized Discounted Cumulative Gain](https://en.wikipedia.org/wiki/Discounted_cumulative_gain) (NDCG)
- [Jaccard Index](https://en.wikipedia.org/wiki/Jaccard_index)
- Per class [Precision](https://en.wikipedia.org/wiki/Precision_and_recall) (class 0 = non-duplicate image pairs, class 1 = duplicate image pairs)
- Per class [Recall](https://en.wikipedia.org/wiki/Precision_and_recall) (class 0 = non-duplicate image pairs, class 1 = duplicate image pairs)
- Per class [f1-score](https://en.wikipedia.org/wiki/F1_score) (class 0 = non-duplicate image pairs, class 1 = duplicate image pairs)

The api for obtaining these metrics is as below:
```python
Expand Down Expand Up @@ -375,12 +386,29 @@ function with scores attribute set to True).
- *outfile*: Name of the file the plot should be saved to. *None* by default.

The output looks as below:
![figs](/Users/tanuj.jain/Documents/dedup-data/readme_dups.png)
![figs](_readme_figures/plot_dups.png)

## Contribute
We welcome all kinds of contributions, models trained on different datasets, new model architectures and/or hyperparameters combinations that improve the performance of the currently published model.

Will publish the performances of new models in this repository.

See the [Contribution](CONTRIBUTING.md) guide for more details.

## Citation
Please cite Imagededup in your publications if this is useful for your research. Here is an example BibTeX entry:
```
@misc{idealods2019imagededup,
title={Imagededup},
author={Tanuj Jain and Christopher Lennan and Zubin John},
year={2019},
howpublished={\url{https://github.com/idealo/image-dedup}},
}
```

## Maintainers
* Tanuj Jain, github: [tanujjain](https://github.com/tanujjain)
* Christopher Lennan, github: [clennan](https://github.com/clennan)

## Copyright
See [LICENSE](LICENSE) for details.
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