OpenMMLab covers a wide range of research topics of computer vision, such as classification, detection, segmentation, and super-resolution. Accuracy, speed, ease of deployment, and a permissive license make RTMDet an ideal model for enterprise users building commercial applications. Importantly, RTMDet is distributed through MMDetection and MMYOLO packages under the Apache-2.0 license. This design enhances the model’s ability to capture global context while maintaining fast inference speed. RTMDet utilizes an architecture with compatible capacities in both the backbone and neck, constructed using a basic building block comprising large-kernel depth-wise convolutions. 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 is an efficient real-time object detector, with self-reported metrics outperforming the YOLO series. Today I'm going to show you how to train an RTMDet - a model that is fast and accurate enough to compete with top models, but which - due to its open license - you can use anywhere. Most popular models come with a license that forces you to open-source your entire project. Looking for a state-of-the-art object detector that you can use in an enterprise project is difficult. We created a Google Colab notebook that you can run in a separate tab while reading this blog post, allowing you to experiment and explore the concepts discussed in real time.
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