Compare RTMDet vs YOLOv7 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
RTMDet vs YOLOv7 Comparison Table
| Property | RTMDet | YOLOv7 |
|---|---|---|
| Organization | OpenMMLab | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Dec 2022 | Jul 2022 |
| Context Window | — | — |
| Parameters | 4.8M-94.9M | 6.2M-151.7M |
| License | GPL v3 | GPL v3 |
| Commercial use | commercial use requires open-sourcing your code or a commercial license | commercial use means releasing your source code under GPL-3.0 |
| Vision Tasks | ||
Both support 1 more task | ||
| Model Features | ||
Both support 1 more feature | ||
RTMDet vs YOLOv7: 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.
YOLOv7 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in July 2022 under the GPL-3.0 license. It introduces Extended Efficient Layer Aggregation Networks (E-ELAN) for improved gradient flow in the backbone, and trainable bag-of-freebies techniques including coarse-to-fine lead guided label assignment and auxiliary heads that improve accuracy without adding inference cost.
YOLOv7 achieves 56.8% AP on COCO at 30 FPS on a V100 GPU at the time of release, establishing a strong accuracy-speed tradeoff among real-time detectors. It supports detection, instance segmentation, and pose estimation variants. YOLOv7 is deployable through Roboflow Inference and the standard training pipeline in the official repository.
