# PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2005.02154 · HTML: https://mlsanity.com/paper/2005.02154/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 5 of 5)

- [Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation](https://mlsanity.com/paper/2102.10935/) (2021)
- [A Fast Partial Video Copy Detection Using KNN and Global Feature Database](https://mlsanity.com/paper/2105.01713/) (2021)
- [Circuit Diagram Retrieval Based on Hierarchical Circuit Graph Representation](https://mlsanity.com/paper/2503.11658/) (2025)
- [Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones](https://mlsanity.com/paper/2101.09412/) (2021)
- [Locality Preserving Markovian Transition for Instance Retrieval](https://mlsanity.com/paper/2506.05196/) (2025)

## Abstract (arXiv)

Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner. In order to fill this gap, we introduce PyRetri, an open source library for deep learning based unsupervised image retrieval. The library encapsulates the retrieval process in several stages and provides functionality that covers various prominent methods for each stage. The idea underlying its design is to provide a unified platform for deep learning based image retrieval research, with high usability and extensibility. To the best of our knowledge, this is the first open-source library for unsupervised image retrieval by deep learning.