Train a Computer Vision Model in ChatGPT with Roboflow

Roboflow’s ChatGPT plugin enables users to manage an end-to-end computer vision workflow conversationally, using an alloy-wheel defect detection example to train RF-DETR Small to identify scratches and paint damage. The process begins by finding and reviewing a labeled public dataset, forking it into a Roboflow workspace, checking dataset health and split quality, and creating a stable baseline dataset version with appropriate resizing and no added augmentation. Users then select a compatible pretrained checkpoint, inspect supported training settings, launch and monitor a baseline run, and evaluate validation metrics including mAP, precision, recall, F1, confusion matrices, and per-image errors. The guide emphasizes that class-level recall and visual review of missed detections are especially important for inspection tasks, where faint scratches may be more consequential than overall accuracy suggests. It also explains how confidence thresholds alter the tradeoff between missed defects and false alarms, how to design controlled comparisons while holding most variables fixed, and how to improve data or training settings based on evidence rather than assumptions. After selecting a model and operating threshold using validation data, users evaluate once on a held-out test set and produce a traceable report documenting dataset versions, configurations, metrics, limitations, failure examples, and proposed next steps.

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