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😎 Finding duplicate images made easy!
| Date | Stars |
|---|---|
| 2026-07-24 | 5656 |
| 2026-07-25 | 5656 |
| 2026-07-28 | 5659 |
| 2026-07-30 | 5659 |
| 2026-08-06 | 5659 |
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# Image Deduplicator (imagededup)
[](https://github.com/idealo/imagededup/actions/workflows/test.yml)
[](https://idealo.github.io/imagededup/)
[](https://codecov.io/gh/idealo/imagededup)
[](https://pypi.org/project/imagededup/)
[](https://github.com/idealo/imagededup/blob/master/LICENSE)
imagededup is a python package that simplifies the task of finding **exact** and **near duplicates** in an image collection.
<p align="center">
<img src="readme_figures/mona_lisa.png" width="600" />
</p>
This package provides functionality to make use of hashing algorithms that are particularly good at finding exact
duplicates as well as convolutional neural networks which are also adept at finding near duplicates. An evaluation
framework is also provided to judge the quality of deduplication for a given dataset.
Following details the functionality provided by the package:
- Finding duplicates in a directory using one of the following algorithms:
- [Convolutional Neural Network](https://arxiv.org/abs/1905.02244#:~:text=MobileNetV3%20is%20tuned%20to%20mobile,improved%20through%20novel%20architecture%20advances.) (CNN) - Select from several prepackaged models or provide your own custom model.
- [Perceptual hashing](http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html) (PHash)
- [Difference hashing](http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html) (DHash)
- [Wavelet hashing](https://fullstackml.com/wavelet-image-hash-in-python-3504fdd282b5) (WHash)
- [Average hashing](http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html) (AHash)
- Generation of encodings for images using one of the above stated algorithms.
- Framework to evaluate effectiveness of deduplication given a ground truth mapping.
- Plotting duplicates found for a given image file.
Detailed documentation for the package can be found at: [https://idealo.github.io/imagededup/](https://idealo.github.io/imagededup/)
imagededup is compatible with Python 3.9+ and runs on Linux, MacOS X and Windows.
It is distributed under the Apache 2.0 license.
## 📖 Contents
- [Installation](#%EF%B8%8F-installation)
- [Quick Start](#-quick-start)
- [Benchmarks](#-benchmarks)
- [Contribute](#-contribute)
- [Citation](#-citation)
- [Maintainers](#-maintainers)
- [License](#-copyright)
## ⚙️ Installation
There are two ways to install imagededup:
- Install imagededup from PyPI (recommended):
```
pip install imagededup
```
- Install imagededup from the GitHub source:
```bash
git clone https://github.com/idealo/imagededup.git
cd imagededup
pip install .
```
## 🚀 Quick Start
In order to find duplicates in an image directory using perceptual hashing, following workflow can be used:
- Import perceptual hashing method
```python
from imagededup.methods import PHash
phasher = PHash()
```
- Generate encodings for all images in an image directory
```python
encodings = phasher.encode_images(image_dir='path/to/image/directory')
```
- Find duplicates using the generated encodings
```python
duplicates = phasher.find_duplicates(encoding_map=encodings)
```
- Plot duplicates obtained for a given file (eg: 'ukbench00120.jpg') using the duplicates dictionary
```python
from imagededup.utils import plot_duplicates
plot_duplicates(image_dir='path/to/image/directory',
duplicate_map=duplicates,
filename='ukbench00120.jpg')
```
The output looks as below:
<p align="center">
<img src="readme_figures/plot_dups.png" width="600" />
</p>
The complete code for the workflow is:
```python
from imagededup.methods import PHash
pExcerpt of 6,345 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0014fc3c6a87c049, topic:neural-network, topic:pytorch
matched fp:0014fc3c6a87c049, topic:computer-vision
