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This tutorial walks through applying pretrained computer vision models (object detection and segmentation) to images, with Codex, an AI coding agent, writing and running the code from natural-language prompts. The practical value is in the workflow: finding models in a Roboflow workspace or Universe, running inference, and generating annotated outputs and metrics. Readers should note that it is a setup guide for a vendor plugin, so the results depend on the quality of the chosen model and the test images.
How to Run Inference in Codex with the Roboflow Plugin

Run computer vision inference from Codex using the Roboflow plugin. Learn to select trained models from your workspace or Roboflow Universe and use simple prompts to run inference on sample images and create annotated images, prediction tables, and visual analytics for your computer vision projects.
Run inference in Codex with the Roboflow plugin: find a trained model, run detection or segmentation on your images, and visualize the results. Running inference means applying a trained model to an image to generate predictions. Codex is OpenAI’s coding agent: you describe a task in plain language, and it writes and runs the code for you. With the Roboflow plugin installed, Codex can find a trained model in your Roboflow workspace or on Roboflow Universe, run inference, and visualize the results, all from natural-language prompts. With the Roboflow plugin (opens in a new tab) in Codex, you can select a trained model, run inference, and visualize the results using natural-language prompts. In this tutorial, we will start with a simple example using an existing object detection model from a Roboflow workspace. We will then explore two RF-DETR (opens in a new tab) examples with more detailed visualizations and analytics. Before starting, open Codex with the Roboflow plugin installed and connected. Keep…
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