List of the Top 3 Visual Search Software for Linux in 2025

The VGG Image Search Engine (VISE) serves as an open-source tool that allows users to conduct visual searches across vast collections of images by using specific image regions as search queries. Developed and maintained by the Visual Geometry Group (VGG) at the University of Oxford's Department of Engineering Science, this software is released under a license that supports unrestricted use in both academic and commercial settings. To promote a vibrant open-source community surrounding VISE, we welcome contributions and encourage active involvement in its ongoing enhancement. Contributors can participate by reporting bugs, improving documentation, adding new features, or making refinements through merge requests. The Visual Geometry Group is dedicated to the ongoing development, upkeep, and support of VISE at least until November 2025, ensuring its relevance and utility. Additionally, users are invited to submit their inquiries or report any issues they face with the VISE software through our GitLab issues portal, facilitating a collaborative effort to strengthen the platform further and enhance user experience. This collective engagement not only enriches the project but also cultivates a sense of community among users and developers alike.

We simplify the integration of advanced computer vision capabilities into your applications, devices, and workflows, allowing you to secure a formidable advantage over your competitors. By doing so, you'll transform how you operate and enhance your overall efficiency.

Voxel51 leads the development of FiftyOne, an open-source toolkit aimed at improving computer vision workflows by enhancing the quality of datasets and offering insights into model performance. FiftyOne allows users to delve into, search, and segment their datasets, making it easy to find samples and labels tailored to their requirements. The toolkit integrates smoothly with well-known public datasets like COCO, Open Images, and ActivityNet, while also providing the option to build custom datasets from scratch. Acknowledging that the quality of data is vital for optimal model performance, FiftyOne enables users to identify, visualize, and address the shortcomings of their models effectively. While manually finding annotation errors can be a time-consuming task, FiftyOne simplifies this by automatically identifying and rectifying label mistakes, thus ensuring the creation of high-quality datasets. Furthermore, conventional performance metrics and manual debugging techniques may not scale effectively, which is where the FiftyOne Brain becomes essential, helping users identify edge cases, mine new training samples, and access various advanced features designed to elevate their workflows. Additionally, this sophisticated toolkit not only streamlines the management of datasets but also encourages a more efficient approach to enhancing computer vision projects overall. Ultimately, FiftyOne transforms the landscape of computer vision by providing a robust platform for dataset curation and model optimization.