How to Train RF-DETR on a Custom Dataset

RF-DETR is a recently released transformer-based object detection model architecture developed by Roboflow, which achieves state-of-the-art performance, outperforming models like LW-DETR and YOLOv11 on datasets such as COCO and RF100-VL. The model, which is licensed under Apache 2.0 allowing free commercial use, is documented in a paper available on Arxiv. It notably breaks the 60 mAP barrier on the Microsoft COCO benchmark while maintaining a performance of 25 FPS on an NVIDIA T4 GPU. The guide provides a step-by-step walkthrough for training an RF-DETR model on a custom dataset, using a mahjong tile recognition task as an example. The process involves downloading a dataset in COCO JSON format from Roboflow Universe, installing the RF-DETR SDK, and fine-tuning the model with a recommended NVIDIA A100 GPU. The results demonstrate RF-DETR's ability to accurately detect objects, with visualizations showing close alignment with ground truth data. The guide also suggests using Roboflow Train for optimized training and mentions upcoming support for model deployment through Roboflow Inference and Roboflow Workflows.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.