How to Train RT-DETR on a Custom Dataset with Transformers

RT-DETR, developed by Peking University and Baidu, is a real-time object detection model that outperforms YOLO models in both speed and accuracy, and has been integrated into the Transformers library for easier fine-tuning on custom datasets. This model, released under the Apache 2.0 license, is particularly beneficial for enterprise projects and has been benchmarked as superior on the RF100-VL benchmark. The tutorial accompanying the model provides detailed guidance on setting up the environment, handling datasets, and applying data augmentations using the albumentations package to enhance model accuracy. It also covers the training process using PyTorch and the evaluation of the model's performance, which achieved a near 0.89 mAP, comparable to other top real-time detectors like YOLOv8. The article highlights how RT-DETR's integration into the Transformers library has simplified the process of training on custom datasets, making it an attractive option for open-source projects in object detection.