RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer

• URL: http://arxiv.org/abs/2407.17140v1
• Date: Wed, 24 Jul 2024 10:20:19 GMT
• Title: RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer
• Authors: Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu,
• Abstract summary: RT-DETRv2 builds upon the previous state-of-the-art real-time detector, RT-DETR. To improve the flexibility, we suggest setting a distinct number of sampling points for features at different scales. To enhance practicality, we propose an optional discrete sampling operator to replace the grid_sample operator.
• Score: 2.1186155813156926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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