TL;DR
RT-DETRv2 introduces several improvements over previous real-time detection transformers, including flexible multi-scale feature sampling, deployment-friendly sampling operators, and adaptive training strategies, resulting in enhanced performance and practicality.
Contribution
The paper presents RT-DETRv2, a new real-time detection transformer with innovative sampling and training techniques for better accuracy and deployment ease.
Findings
Enhanced detection accuracy over previous models
Reduced deployment constraints compared to RT-DETR
Effective training with dynamic data augmentation
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…
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Taxonomy
MethodsSoftmax · Attention Is All You Need · Does Robinhood have a live chat? Live^SuPPorT^NOW · Sparse Evolutionary Training
