  In this report, we present RT-DETRv2, an improved Real-Time DEtection
TRansformer (RT-DETR). RT-DETRv2 builds upon the previous state-of-the-art
real-time detector, RT-DETR, and opens up a set of bag-of-freebies for
flexibility and practicality, as well as optimizing the training strategy to
achieve enhanced performance. To improve the flexibility, we suggest setting a
distinct number of sampling points for features at different scales in the
deformable attention to achieve selective multi-scale feature extraction by the
decoder. To enhance practicality, we propose an optional discrete sampling
operator to replace the grid_sample operator that is specific to RT-DETR
compared to YOLOs. This removes the deployment constraints typically associated
with DETRs. For the training strategy, we propose dynamic data augmentation and
scale-adaptive hyperparameters customization to improve performance without
loss of speed. Source code and pre-trained models will be available at
https://github.com/lyuwenyu/RT-DETR.
