Train a Computer Vision Model in ChatGPT with Roboflow

Train a computer vision model in ChatGPT with the Roboflow plugin: fork a dataset, train RF-DETR, read per-class recall, and fix missed defects. A defect detection model can find obvious paint damage and still miss the faint scratches an inspector needs to catch. Building a useful inspection system means understanding where the model succeeds, where it fails, and what to change next. With the Roboflow plugin in ChatGPT (opens in a new tab), you can explore datasets, train computer vision models, and evaluate their predictions through a conversation. Ask questions about your labels, inspect missed detections, or compare training runs. Roboflow’s training and evaluation skill helps guide those decisions, while the connected tools perform the work in your workspace. In this guide, we’ll train an RF-DETR model (opens in a new tab) to detect scratches and paint damage on alloy wheels. We’ll start with a labeled dataset from Roboflow Universe (opens in a new tab), prepare a dataset version, and train a baseline model. Then we’ll examine its errors, explore confidence thresholds, and use the results to choose and evaluate our next improvement. Connect the Roboflow plugin in ChatGPT and sign in to your Roboflow account. Confirm the workspace you…