Model Cascades for Computer Vision: Building a Two-Stage Vision Pipeline

A Roboflow Workflows tutorial describes building a two-stage ceramic tile inspection cascade that uses RF-DETR to identify defects quickly, Gemini to review uncertain detections, and human inspectors to resolve unclear cases. RF-DETR accepts detections at or above a configurable confidence threshold, commonly 0.70, ignores very low-confidence results below 0.40, and sends intermediate cases to Gemini, whose responses are treated as labels rather than reliable confidence scores; invalid or “unsure” responses are routed to review. The guide compares sending Gemini full images, marked full images, or crops of uncertain boxes, recommending crop-and-stitch processing for close inspection of small defects while noting that it cannot catch defects missed by the initial detector. It explains how to merge accepted and Gemini-confirmed defects into pass, reject, or inspect verdicts, upload uncertain images with pre-labels for annotation and retraining, and monitor escalation rates, latency, cost, and model agreement. Although cascades can reduce unnecessary manual inspection and cloud-model use, they may be unsuitable when most detections are uncertain, line-speed rejection is required, images cannot leave a facility, volumes are low, or a specialized detector alone can meet the task’s requirements.

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