Detecting and Editing Visual Objects with Gemini | Towards Data Science

Towards Data Science

by Laurent Picard

February 26, 2026

AI-Generated Deep Dive Summary

This article explores how Google's Gemini AI model can revolutionize visual object detection and image editing by leveraging its open-vocabulary capabilities. Traditional computer vision models require extensive training on specific object classes, making it time-consuming to detect novel objects. However, Gemini enables users to identify objects using natural language descriptions, eliminating the need for custom datasets and reducing manual effort. The article highlights challenges in processing unstructured visual data from books and magazines, such as variations in style, distortions, and noise. The proposed solution involves a robust pipeline that detects, extracts, and edits objects using Gemini's spatial understanding and image editing tools like Nano Banana models. This approach transforms low-quality images into high-resolution assets, making it ideal for creative industries. The implementation details include Python packages such as google-genai for API access and pillow for image management. Users can integrate Gemini via Vertex AI or Google AI Studio, with options for free tier usage and pay-as-you-go services for advanced features. The open-source code provided under Apache 2.0 encourages experimentation, fostering innovation in AI-driven image processing. For AI enthusiasts, this article underscores the potential of combining language models with computer vision to solve complex problems efficiently. By automating tasks that were previously labor-intensive, Gemini democratizes access to powerful visual editing tools, enabling broader applications across industries.

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Originally published on Towards Data Science on 2/26/2026