Pyyan / Compare / YOLO-World vs RF-DETR vs YOLO26 vs YOLOv12 vs RTMDet

YOLO-World vs RF-DETR vs YOLO26 vs YOLOv12 vs RTMDet

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Object Detection · verified 13 Aug 2026

×YOLO-WorldTencentcurrent
×RF-DETRRoboflowcurrent
×YOLO26Ultralyticscurrent
×YOLOv12Ultralyticscurrent
×RTMDetOpenMMLabcurrent
SpecificationYOLO-WorldRF-DETRYOLO26YOLOv12RTMDet
SummaryOpen-vocabulary detection at YOLO speed.First real-time detector past 60 mAP on COCO.The latest in the line most people mean by object detection.Attention-centric YOLO, still widely deployed.Pure throughput, and an MIT licence.
COCO mAP~46 zero-shot60.5~55~55~52
SpeedHighReal timeVery highVery high300+
LicenceGPL-3.0Apache 2.0AGPL-3.0AGPL-3.0MIT
FamilyOpen vocabularyTransformer, set predictionSingle stageSingle stageSingle stage
Open vocabularyYesNoNoNoNo
Released20242025202620252022
CategoryObject DetectionObject DetectionObject DetectionObject DetectionObject Detection
OfficialTencentRoboflowUltralyticsUltralyticsOpenMMLab

Highlighted rows are where these differ.

YOLO-World

  • The speed of YOLO with text-defined classes
  • Weaker than Grounding DINO on hard prompts

Best for prompted detection that has to be fast.

Full spec sheet →

RF-DETR

  • Leads the RF100-VL domain-transfer benchmark
  • DINOv2 backbone, markedly better on occluded objects
  • Apache 2.0, which YOLO is not

Best for most custom detection tasks.

Full spec sheet →

YOLO26

  • Strong on Jetson, Snapdragon and ARM CPUs
  • AGPL-3.0: open-source your derivative work or buy a licence

Best for edge and mobile deployment.

Full spec sheet →

YOLOv12

  • Attention added to the classic single-stage design
  • Same licensing consideration as YOLO26

Best for teams already on the YOLO toolchain.

Full spec sheet →

RTMDet

  • Wins on raw speed where accuracy is sufficient
  • MIT, so no derivative-work obligation

Best for high frame-rate video.

Full spec sheet →