# When Does Supervised Training Pay Off? The Hidden Economics of Object Detection in the Era of Vision-Language Models

arXiv: https://arxiv.org/abs/2510.11302 · HTML: https://mlsanity.com/paper/2510.11302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Driving VLMs and open-vocabulary detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Driving VLMs and open-vocabulary detection](https://mlsanity.com/mllm/driving-vlms-and-open-vocabulary-detection/)

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)

## Abstract (arXiv)

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.