We have found the Needle in haystack! 🪡🔍
Needle is an open-source image search engine that finds pictures from a description in plain language. It runs as a desktop app on your own machine — no accounts, no cloud, no Docker, and nothing uploaded. Point it at a folder, describe the picture you’re after, and it finds the closest matches.
Born from research on high-accuracy retrieval, Needle outperforms state-of-the-art methods on complex natural-language queries while staying approachable enough to install and use in a couple of minutes. ✨
How it works. Needle doesn’t match your words against text or filenames. It generates a small preview image from your query, embeds it with the same models used to index your library, and retrieves the images that look closest. Comparing images to images is what lets it handle descriptions that tagging and keyword search cannot.
- 🔍 Search in plain language across your own folders.
- 🖥️ Fully local — your photos never leave your machine.
- 🎨 Built-in image generation, on-device and optional.
- 📁 Stays in sync — folders are watched, so new and deleted images are picked up automatically.
- 🍎🐧🪟 macOS, Linux and Windows installers.
See Needle in Action
*Watch as Needle transforms natural language queries into precise image retrieval results in real time.*🎨 Try the Interactive Demo
Experience Needle’s full capabilities with our interactive demo! Test different queries, explore the interface, and see how Needle works with real data.
The demo showcases sample queries, image generation, and similarity search with pre-processed datasets.
Comparison to State-of-the-Art Methods
Curious how Needle measures up against other cutting-edge approaches? Here, you’ll soon find performance plots that compare Needle with OPEN-AI CLIP image retrieval method for LVIS, Caltech256 and BDD100k.
Mean Average Precision Across Datasets (All Queries)
Mean Average Precision Across Datasets (Hard Queries)
User Study Preferences
Get Started Today!
Ready to revolutionize your image retrieval process? 🚀
Install and test Needle now to experience the future of multimodal search!
Tip: For detailed installation instructions, check out the Getting Started section.
Cite us
For a deep dive into Needle’s theoretical guarantees and performance insights, please refer to our research paper.
If you find Needle beneficial for your work, we kindly ask that you cite our work to help support continued innovation.
@article{erfanian2024needle,
title={Needle: A Generative-AI Powered Monte Carlo Method for Answering Complex Natural Language Queries on Multi-modal Data},
author={Erfanian, Mahdi and Dehghankar, Mohsen and Asudeh, Abolfazl},
journal={arXiv preprint arXiv:2412.00639},
year={2024}
}