RT DETRv2 Overview The RT DETRv2 model was proposed in RT DETRv2: Improved Baseline with Bag of Freebies for Real Time Detection Transformer by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu. RT DETRv2 refines RT DETR by introducing selective multi scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real time performance. This model was contributed by @jadechoghari with the help of @cyrilvallez and @qubvel hf This is Performance RT DETRv2 consistently outperforms its predecessor across all model sizes while maintaining the same real time speeds. How to use Training RT DETRv2 is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval50 commonly used in real scenarios. Applications RT DETRv2 is ideal for real time object detection in diverse applications such as autonomous driving , sur…
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