{"ID":2856301,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11302","arxiv_id":"2510.11302","title":"When Does Supervised Training Pay Off? The Hidden Economics of Object Detection in the Era of Vision-Language Models","abstract":"Object detection traditionally relies on costly manual annotation. We present the first comprehensive cost-effectiveness analysis comparing supervised YOLO and zero-shot vision-language models (Gemini Flash 2.5 and GPT-4). Evaluated on 5,000 stratified COCO images and 500 diverse product images, combined with Total Cost of Ownership modeling, we derive break-even thresholds for architecture selection. Results show supervised YOLO attains 91.2% accuracy versus 68.5% for Gemini and 71.3% for GPT-4 on standard categories; the annotation expense for a 100-category system is $10,800, and the accuracy advantage only pays off beyond 55 million inferences (151,000 images/day for one year). On diverse product categories Gemini achieves 52.3% and GPT-4 55.1%, while supervised YOLO cannot detect untrained classes. Cost-per-correct-detection favors Gemini ($0.00050) and GPT-4 ($0.00067) over YOLO ($0.143) at 100,000 inferences. We provide decision frameworks showing that optimal architecture choice depends on inference volume, category stability, budget, and accuracy requirements.","short_abstract":"Object detection traditionally relies on costly manual annotation. We present the first comprehensive cost-effectiveness analysis comparing supervised YOLO and zero-shot vision-language models (Gemini Flash 2.5 and GPT-4). Evaluated on 5,000 stratified COCO images and 500 diverse product images, combined with Total Cos...","url_abs":"https://arxiv.org/abs/2510.11302","url_pdf":"https://arxiv.org/pdf/2510.11302v2","authors":"[\"Samer Al-Hamadani\"]","published":"2025-10-13T11:48:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Language Model\"]","has_code":false}