RT-DETR vs YOLO11
Compare RT-DETR and YOLO11 side-by-side.
Compare RT-DETR vs YOLO11 live
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These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
RT-DETR vs YOLO11 Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | RT-DETR | YOLO11 |
|---|---|---|
| Organization | Baidu | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Apr 2023 | Sep 2024 |
| Context Window | — | — |
| Parameters | 20M-76M | 2.6M-56.9M |
| License | Apache 2.0 | AGPL 3.0 |
| Model Sizes input resolution per size variant | ||
| Nano | 640×640 | |
| Small | 1280×1280, 640×640 | |
| Medium | 1280×1280, 640×640 | |
| Large | 1280×1280, 640×640 | |
| XL | 1280×1280, 640×640 | |
| Vision Tasks | ||
| Object Detection | Demo (COCO) | |
| Instance Segmentation | Demo (COCO) | |
| Model Features | ||
| Real-Time Vision | ||
RT-DETR vs YOLO11: Overview
RT-DETR (Real-Time Detection Transformer) is an object detection model developed by Baidu, released in April 2023 under the Apache 2.0 license. It is the first transformer-based real-time object detector, addressing the inference speed limitations of earlier DETR models through an efficient hybrid encoder that decouples intra-scale interaction and cross-scale fusion, enabling the model to process multi-scale features without the high computational overhead of standard transformer encoders.
RT-DETR achieves 53.1% AP on COCO at 108 FPS on an NVIDIA T4 GPU for the RT-DETR-L variant, outperforming comparably sized YOLO detectors at similar speeds. It maintains end-to-end inference without non-maximum suppression, simplifying deployment pipelines. RT-DETR established the baseline for real-time transformer detection and has been extended by subsequent works including RF-DETR and RT-DETRv2.
YOLO11 is an object detection and multi-task vision model developed by Ultralytics, released in September 2024 under the AGPL-3.0 license. It is the latest generation in the Ultralytics YOLO series and supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a single unified framework. YOLO11 introduces architectural refinements that improve accuracy while reducing parameter count compared to YOLOv8 at equivalent model sizes.
YOLO11 is available in five model sizes from Nano to Extra Large and is deployable through the Ultralytics Python package, Roboflow Inference, and export formats including ONNX, TensorRT, and CoreML. It supports fine-tuning on custom datasets through the standard Ultralytics training API.