How to Train RTMDet on a Custom Dataset

The blog post by Piotr Skalski provides an in-depth tutorial on training the RTMDet model, a state-of-the-art object detector, using the OpenMMLab ecosystem. RTMDet stands out for its speed and accuracy, operating at over 300 FPS on an NVIDIA 3090 GPU with a 52.8% Average Precision on the COCO dataset, and is available under an Apache-2.0 license, making it suitable for enterprise applications. The post guides users through setting up the necessary environment with libraries from OpenMMLab, such as MMDetection and MMYOLO, and explains the process of downloading datasets from Roboflow Universe in COCO format for training. It also covers the preparation of a custom configuration file, training the model using a script, and evaluating the model's performance using metrics like mean average precision and a confusion matrix. The tutorial is complemented by a Google Colab notebook to facilitate real-time experimentation with the concepts discussed.