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本文主要记录了使用rf-detr这一新型目标检测模型架构进行训练自己搭建的数据集的过程，并给出了相应的实现代码以及训练过程中可能遇到的报错情况和解决方法，此外还给出了将YOLO数. RF-DETR 是由 Roboflow 开发的基于 Transformer 的实时目标检测模型架构。 RF-DETR 系列模型在所有尺寸的目标检测模型中都是最快和最精确的。 然而， Roboflow 推出的 RF-DETR （Real-Time Detection Transformer）以更高的准确率和优化的速度表现，重新定义了实时目标检测的标准。 作为一个开源且支持商用的模.

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RT-DETR和RF-DETR突破实时检测瓶颈，前者混合编码器优化性能，后者成首个COCO上AP超60的实时模型。 二者均无需NMS，兼顾精度与速度，可在Coovally平台一键调. RF-DETR是Roboflow推出的新一代实时目标检测模型，属于DETR（Detection Transformer）家族。 它首次在COCO数据集上实现了60+的平均精度均值（mAP），同时保. RF-DETR是首个在COCO数据集上突破60 mAP的实时检测模型，结合Transformer架构与DINOv2主干网络，支持多分辨率灵活切换，为安防、自动驾驶等场景提供.