Crop Regions of Interest & Stitch Them Before VLM Inference

A Roboflow Workflows tutorial describes a carton-damage inspection pipeline that uses an RF-DETR object detector to identify candidate damage regions, crops them from the original image, combines up to ten numbered crops into collage pages, and sends those pages to Gemini for structured per-region assessments. Numbered ROI IDs and stored bounding-box mappings allow Gemini findings to be linked back to their original locations, while filtering, conditional execution, JSON parsing, and a reporting block help validate that all expected regions receive unique, valid results. The approach is intended to reduce repeated prompts and image inputs compared with sending each crop in a separate VLM request, but it cautions that actual token and cost savings depend on model resolution, output usage, retries, and measured provider billing rather than a fixed per-image token estimate. The tutorial recommends testing the workflow on labeled images, comparing collage and separate-crop versions under consistent settings, and evaluating detection quality separately from VLM judgments. It also emphasizes preserving crop context, avoiding overly crowded collages or obscuring number badges, handling uncertain findings, and validating zero-ROI and multi-page cases before relying on reported defect totals.

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