Detecting and Editing Visual Objects with Gemini

2026-02-26 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

This article explores using Google's Gemini models for open-vocabulary object detection and image editing, bypassing the need for traditional computer vision model training. It demonstrates how Gemini's spatial understanding capabilities can detect diverse objects, such as illustrations in historical books or electronic components on a circuit board, based solely on natural language prompts. The detected objects are then extracted and transformed using Gemini's Nano Banana image editing models, performing tasks like artifact restoration, colorization, and style transformation into cinematic stills. The approach is showcased through various examples, highlighting its flexibility and precision in handling challenges like image distortion, noise, and varied content styles, all with minimal code using the Python SDK and structured JSON outputs.

Key takeaway

For AI Engineers and Machine Learning Engineers building image processing pipelines, this approach fundamentally changes how you might tackle object detection and image manipulation. You should explore Gemini's open-vocabulary detection and Nano Banana's editing capabilities to rapidly prototype and deploy solutions for diverse visual content, significantly reducing development time and resource overhead associated with custom model training and dataset labeling. Consider integrating structured outputs and iterative prompt refinement for robust, production-ready workflows.

Key insights

Gemini enables open-vocabulary object detection and advanced image editing using natural language prompts, eliminating traditional model training.

Principles

• Open-vocabulary detection adapts to diverse objects without retraining.
• Descriptive prompts can simplify complex image transformations.
• Spatial understanding works at the pixel level for precise detection.

Method

Define object detection criteria and output schema in a natural language prompt, then use Gemini's multimodal models for detection and Nano Banana models for subsequent image restoration and stylistic editing.

In practice

• Use `response_mime_type="application/json"` for structured outputs.
• Specify `media_resolution=PartMediaResolutionLevel.MEDIA_RESOLUTION_ULTRA_HIGH` for fine details.
• Iteratively refine descriptive prompts for complex transformations.

Topics

• Gemini Models
• Object Detection
• Image Editing
• Open-Vocabulary Detection
• Generative AI

Code references

• GoogleCloudPlatform/generative-ai

Best for: AI Engineer, Machine Learning Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.