YOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family

Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specific components impose placement constraints absent from regular Transformer stacks. We propose YOLO-PEFT, a structure-aware framework that formulates adapter placement as an auditable constraint-planning problem. Given a detector graph, a PEFT request, and a resource budget, YOLO-PEFT assigns operator and semantic roles

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ethics.ai (6 August 2026), “YOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family,” evidence record 17776, https://ethics.ai/record/17776 (originally published by HuggingFace Daily Papers).

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