RTMDet vs YOLOv8
Compare RTMDet and YOLOv8 side-by-side.
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Models in this comparison
RTMDet vs YOLOv8 Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | RTMDet | YOLOv8 |
|---|---|---|
| Organization | OpenMMLab | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Dec 2022 | Jan 2023 |
| Context Window | — | — |
| Parameters | 4.8M-94.9M | 3.2M-68.2M |
| License | GPL v3 | AGPL 3.0 |
| Model Sizes input resolution per size variant | ||
| Nano | 1280×1280, 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) | |
| Model Features | ||
| Real-Time Vision | ||
RTMDet vs YOLOv8: Overview
RTMDet is a real-time object detection model developed by OpenMMLab, released in December 2022 under the GPL-3.0 license. It adopts a single-stage detection architecture with large-kernel depthwise convolution in both the backbone and neck, enabling it to capture long-range spatial dependencies without the computational cost of full self-attention. The model family spans from RTMDet-tiny to RTMDet-x, covering a wide range of speed-accuracy operating points.
RTMDet-x achieves 52.6% AP on COCO at 114 FPS on an NVIDIA 3090 GPU. The architecture supports instance segmentation and rotated object detection variants. RTMDet is included in the OpenMMLab ecosystem and is well suited for applications requiring fast, accurate detection with flexible model sizing.
YOLOv8 is an object detection and multi-task vision model developed by Ultralytics, released in January 2023 under the AGPL-3.0 license. It succeeds YOLOv5 and introduces an anchor-free detection head, a new C2f module for improved gradient flow, and a decoupled head that separates classification and regression tasks. These changes improve both accuracy and training efficiency compared to earlier Ultralytics models.
YOLOv8 supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a unified codebase. It is available in five sizes from Nano to Extra Large and exports to ONNX, TensorRT, CoreML, and other formats. YOLOv8 is one of the most widely adopted detection models in production and is directly supported by Roboflow Inference for custom model training and deployment.