How does Agentic Object Detection work?

Upload an image and enter a natural-language prompt; the agent analyzes the image and returns bounding boxes in [Xmin, Ymin, Xmax, Ymax] format.

How fast are the results?

Typical processing time is about 20–30 seconds per image, depending on prompt complexity and image size.

What kinds of objects or conditions can it detect?

Everyday objects and nuanced cases, including intrinsic attributes (e.g., color, shape), specific identities, contextual relationships, and dynamic states.

Does it require dataset labeling or training?

No. It enables zero-shot detection based solely on text prompts, eliminating lengthy labeling and training cycles.

How accurate is it compared to other systems?

On Landing AI’s internal benchmark, it achieves a 79.7% F1 score, outperforming models like Microsoft Florence-2 and Google OWLv2.

Is it part of LandingLens?

Yes. It powers object detection within LandingLens, facilitating tracking, counting, and spatial understanding workflows.

Is there a way to try it before integrating?

Yes. Use the free web playground to test prompts on sample or uploaded images, and explore the demo API for quick trials.

Is there an API for developers?

Yes. An API lets you submit images and prompts and receive detection outputs programmatically for apps and pipelines.

What makes it different from traditional detectors?

Its agentic reasoning enables zero-shot, prompt-based detection of complex attributes, relationships, and states—without task-specific training.

Agentic Object Detection

Name: Agentic Object Detection
Brand: Agentic Object Detection
Availability: InStock
Rating: 5 (1 reviews)
Author: Agentic Object Detection

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Last updated: April 6, 2026

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What is Agentic Object Detection?

Agentic Object Detection by Landing AI is a reasoning-driven, zero-shot computer vision tool integrated with LandingLens that detects complex objects from natural-language prompts—covering intrinsic attributes (e.g., “unripe strawberry”), specific identities (e.g., “hex key set”), contextual relationships (e.g., “daisy on top of ice cream”), and dynamic states (e.g., “player in mid-air”)—returning bounding boxes in seconds, outperforming competitors with a 79.7% F1 score and offering a playground and API for rapid prototyping and app integration.