Top 10 Open Source Alternatives to StockAI Image Search in 2025

As the landscape of open-source tools continues to evolve, 2025 has introduced several alternatives to StockAI Image Search that offer unique capabilities and improved functionalities. This article delves into the top 10 open-source image search tools, discussing their features, data points, and links to learn more.

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1. Milvus

Description: Milvus is an open-source vector database optimized for storing and searching high-dimensional vectors used to represent images.
Functionality: Converts images into vector embeddings using models like ResNet-50, stores them, and compares new images to the stored vectors for similarity searches.
Data Points:
Vector Database: Uses Milvus for vector storage and comparison.
ML Models Used: Utilizes models such as YOLOv3 for object detection and ResNet-50 for image feature extraction.
Database Mapping: Employs MySQL to map vector IDs to original images.
Scalability: Designed for efficient handling of large datasets.
Use Case: Suited for e-commerce and similar platforms needing image pattern detection. Learn more about Milvus

2. RevEye Reverse Image Search (Browser Extension)

Description: RevEye is a browser extension that allows users to right-click on any image on a webpage and perform reverse image searches using multiple search engines.
Functionality: Supports multiple search engines like Google, Bing, Yandex, and TinEye. Users can also add custom search engines.
Data Points:
Search Engines: Supports Google Lens, Bing, Yandex, TinEye, and user-defined engines.
Open Source: Yes, the source code is available.
Browser Extension: Integrates directly into the browser for easy use.
Privacy: Does not track user data or include ads.
Customizable: Allows users to configure the context menu. Explore RevEye on GitHub

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3. FAISS (Facebook AI Similarity Search)

Description: FAISS is an open-source library developed by Meta that provides tools for efficient similarity search and clustering of dense vectors.
Functionality: Indexes datasets using vector embeddings to retrieve similar photos.
Data Points:
Library Type: Open-source library for similarity search and clustering.
Developed by: Meta (Facebook AI).
Efficiency: Designed for fast and efficient processing of large vector datasets.
Indexing: Uses an "Index object" for vector database storage.
Use Case: Used to retrieve photos that resemble the query. Discover FAISS on GitHub

4. QISS (Open Source Image Similarity Search Engine)

Description: QISS is a multi-lingual image similarity search engine that uses dual-path neural networks to embed texts and images into a common feature space.
Functionality: Allows searches using both text and image queries.
Data Points:
Multi-lingual Support: Designed to handle multiple languages.
Dual-Path Neural Networks: Uses deep learning for image and text embedding.
Feature Space: Embeds both images and texts into a shared feature space.
Search Capabilities: Supports both image-based and text-based searches.
Research based: Described in a research paper in the National Library of Medicine. Read the research paper on QISS

5. DeepDetect

Description: DeepDetect is an open-source machine learning API and server written in C++. It provides image similarity search functionality.
Functionality: Can classify images using pre-trained models like ResNet50, then find similar images within the same class.
Data Points:
Machine Learning API: Serves as an API for machine learning tasks.
Written in C++: Provides optimized performance for demanding tasks.
Pre-trained Models: Uses ResNet50 model for image classification.
Similarity Search: Finds similar images based on classification.
Open Source: Yes. Explore DeepDetect on GitHub

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6. PhotoPrism

Description: PhotoPrism is a self-hosted photo management software that offers features similar to Google Photos, including image search based on person, thing, and location.
Functionality: Organizes and categorizes photos, and it does so locally.
Data Points:
Self-Hosted: Can be hosted on your own server, providing more privacy.
Automatic Tagging: Identifies people, objects, and locations.
Categorization: Organizes photos and provides search capabilities based on several criteria.
Free for Self-Hosting: Free if you host it yourself; Paid for hosted versions.
Google Photos alternative: Is a good replacement for Google Photos. Learn more about PhotoPrism

7. Search by Image (Browser Extension)

Description: A browser extension that supports reverse image searches through a variety of search engines.
Functionality: Allows right-click reverse image searches for multiple search engines such as Google, Bing, Yandex, Baidu, and TinEye.
Data Points:
Multiple Search Engines: Supports over 30 search engines, including the main ones and more.
Open Source: Community-driven and open source.
Browser Extension: Integrated into the browser for convenience.
OSINT Tool: Helps verify image authenticity.
Community Driven: Made possible by a community of supporters. Download Search by Image on SourceForge

8. VGG Image Search Engine (VISE)

Description: VISE is a free, open-source software developed by the Visual Geometry Group at Oxford University. It allows visual searching of large image collections using an image region as a query term.
Functionality: Enables searching using specific regions of images, not just the full image.
Data Points:
Region-Based Search: Allows searching based on image regions.
Open Source: Yes.
Developed by: Visual Geometry Group (VGG) at Oxford University.
License: Under an unrestricted license for academic and commercial use.
Research-Oriented: Designed for academic and industrial projects. Learn more about the Visual Geometry Group

9. Simple Image Search Engine (sis)

Description: A simple image-based search engine using Keras and Flask, that extracts a deep-feature from each database image.
Functionality: Uses VGG16 model for feature extraction and a linear scan for finding similar images.
Data Points:
Framework: Uses Keras for ML and Flask for web interface.
Deep-Feature Extraction: Employs a VGG16 model to extract deep features from the images.
No GPU Required: Can run on a regular CPU.
Linear Scan: Uses a linear scan for similarity searches, making it simpler but slower with large datasets.
Easy to setup: Only requires running two Python scripts. Discover Simple Image Search Engine on GitHub

10. Reverse Image Search Engine (GitHub project)

Description: A GitHub project that utilizes EfficientNet-B0 for vector embedding and ChromaDB for vector storage and retrieval.
Functionality: Enables reverse image search functionality.
Data Points:
Vector Embedding: Uses EfficientNet-B0 for efficient and accurate image feature representation.
Vector Database: Employs ChromaDB for efficient vector storage and retrieval.
Tech Stack: Built using Python, Pytorch, transformers, and Streamlit.
Web and CLI: Accessible via browser or command line.
Open Source: Yes. Explore Reverse Image Search Engine on GitHub

These 10 options provide a range of solutions for your image search needs, from browser extensions to full-fledged self-hosted tools and libraries. They each offer different features and levels of complexity, so you should be able to find one that suits your specific requirements.

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FAQ

1. What is Milvus?

Milvus is an open-source vector database for building reverse image search systems. It uses models like ResNet-50 to convert images into vector embeddings, which are stored and compared for similarity searches. Learn more about Milvus

2. What is RevEye Reverse Image Search?

RevEye is an open-source browser extension that performs reverse image searches using multiple engines like Google, Bing, Yandex, and TinEye. Explore RevEye on Chrome Web Store

3. What is FAISS by Meta?

FAISS is an efficient similarity search and clustering library developed by Meta (Facebook AI) for handling large vector datasets. Discover more about FAISS | FAISS GitHub

4. What is QISS?

QISS is a multi-lingual image similarity search engine that uses dual-path neural networks to embed texts and images into a shared feature space, allowing both image-based and text-based searches. Read about QISS on NIH

5. What is DeepDetect?

DeepDetect is an open-source ML API and server offering image similarity search functionality using pre-trained models like ResNet50. Learn more about DeepDetect | DeepDetect GitHub

6. What is PhotoPrism?

PhotoPrism is self-hosted photo management software with features similar to Google Photos, including local image search by person, thing, and location. Discover PhotoPrism

7. What is the Search by Image extension?

Search By Image is a browser extension supporting reverse image searches with over 30 engines, including Google, Bing, Yandex, Baidu, and TinEye. Explore Search by Image | Mac App Store

8. What is VGG Image Search Engine (VISE)?

VISE is an open-source software by the Visual Geometry Group at Oxford University for visual searching of large image collections using image regions. Learn more about VISE at Oxford

9. What is the Simple Image Search Engine (sis)?

The Simple Image Search Engine (sis) uses Keras and Flask to create an image-based search engine with feature extraction powered by the VGG16 model. Discover sis on GitHub

10. What is the Reverse Image Search Engine project on GitHub?

This GitHub project enables reverse image search using EfficientNet-B0 for vector embedding and ChromaDB for vector storage and retrieval. Explore the project on GitHub

References

About the Author

Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.

Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).

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About the Publication

Fe/male Switch is an innovative startup platform designed to empower women entrepreneurs through an immersive, game-like experience. Founded in 2020 during the pandemic "without any funding and without any code," this non-profit initiative has evolved into a comprehensive educational tool for aspiring female entrepreneurs.The platform was co-founded by Violetta Shishkina-Bonenkamp, who serves as CEO and one of the lead authors of the Startup News branch.

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Fe/male Switch Foundation was created to address the gender gap in the tech and entrepreneurship space. The platform aims to skill-up future female tech leaders and empower them to create resilient and innovative tech startups through what they call "gamepreneurship". By putting players in a virtual startup village where they must survive and thrive, the startup game allows women to test their entrepreneurial abilities without financial risk.

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