RTMDet is an efficient real-time object detector, with self-reported metrics outperforming the YOLO series. It achieves 52.8% AP on COCO with 300+ FPS on an NVIDIA 3090 GPU, making it one of the fastest and most accurate object detectors available as of writing this post.
Try the Model
Use the widget below to experiment with RTMDet. You can detect COCO classes such as people, vehicles, animals, household items.
Overview
RTMDet is an efficient real-time object detector, with self-reported metrics outperforming the YOLO series. It achieves 52.8% AP on COCO with 300+ FPS on an NVIDIA 3090 GPU, making it one of the fastest and most accurate object detectors available as of writing this post.
RTMDet License
RTMDet
is licensed under a
license.
Performance
Deploy a RTMDet API
You can use Roboflow Inference to deploy a
RTMDet
API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).
Below are instructions on how to deploy your own model API.
Label Data Automatically with RTMDet
You can automatically label a dataset using RTMDet with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use RTMDet to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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Convert Annotation Format
YOLOv8 uses the uses the YOLOv8 PyTorch TXT annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.