RF-DETR: Neural Architecture Search for Real-Time Detection Transformers

• URL: http://arxiv.org/abs/2511.09554v1
• Date: Thu, 13 Nov 2025 02:02:01 GMT
• Title: RF-DETR: Neural Architecture Search for Real-Time Detection Transformers
• Abstract summary: We introduce RF-DETR, a light-weight specialist detection transformer.<n>Our approach fine-tunes a pre-trained base network on a target dataset.<n>We revisit the "tunable knobs" for NAS to improve the transferability of DETRs to diverse target domains.
• Score: 42.015879164246805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight vision-language model (VLM) for new domains, we introduce RF-DETR, a light-weight specialist detection transformer that discovers accuracy-latency Pareto curves for any target dataset with weight-sharing neural architecture search (NAS). Our approach fine-tunes a pre-trained base network on a target dataset and evaluates thousands of network configurations with different accuracy-latency tradeoffs without re-training. Further, we revisit the "tunable knobs" for NAS to improve the transferability of DETRs to diverse target domains. Notably, RF-DETR significantly improves on prior state-of-the-art real-time methods on COCO and Roboflow100-VL. RF-DETR (nano) achieves 48.0 AP on COCO, beating D-FINE (nano) by 5.3 AP at similar latency, and RF-DETR (2x-large) outperforms GroundingDINO (tiny) by 1.2 AP on Roboflow100-VL while running 20x as fast. To the best of our knowledge, RF-DETR (2x-large) is the first real-time detector to surpass 60 AP on COCO. Our code is at https://github.com/roboflow/rf-detr

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