Segment scene fast by change multi-path blocks in training to single-path when infer with RDRNet
Segment scene fast by change multi-path blocks in training to single-path when infer with RDRNet
Reparameterizable Dual-Resolution Network for Real-time Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2406.12496
arXiv PDF paper https://arxiv.org/pdf/2406.12496
Semantic segmentation plays a key role in applications such as autonomous driving and medical image.
Although existing real-time semantic segmentation models achieve a commendable balance between accuracy and speed, their multi-path blocks still affect overall speed.
To address this issue, this study proposes a Reparameterizable Dual-Resolution Network (RDRNet) dedicated to real-time semantic segmentation.
... RDRNet employs a two-branch ... utilizing multi-path blocks during training and reparameterizing ... into single-path ... during inference ... enhancing ... accuracy and inference speed
... propose the Reparameterizable Pyramid Pooling Module (RPPM) to enhance the feature representation of the pyramid pooling module without increasing its inference time.
... demonstrate that RDRNet outperforms existing state-of-the-art models in terms of both performance and speed ...
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