HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

• URL: http://arxiv.org/abs/2010.07621v1
• Date: Thu, 15 Oct 2020 09:32:38 GMT
• Title: HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network
• Authors: Pengcheng Yuan, Shufei Lin, Cheng Cui, Yuning Du, Ruoyu Guo, Dongliang He, Errui Ding and Shumin Han
• Abstract summary: Hierarchical-Split Block can be taken as a plug-and-play block to upgrade existing convolutional neural networks. Our network achieves 81.28% top-1 accuracy with competitive latency on ImageNet-1k dataset.
• Score: 37.56074820823266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses representational block named Hierarchical-Split Block, which can be taken as a plug-and-play block to upgrade existing convolutional neural networks, improves model performance significantly in a network. Hierarchical-Split Block contains many hierarchical split and concatenate connections within one single residual block. We find multi-scale features is of great importance for numerous vision tasks. Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications. In this work, we present a common backbone based on Hierarchical-Split block for tasks: image classification, object detection, instance segmentation and semantic image segmentation/parsing. Our approach shows significant improvements over all these core tasks in comparison with the baseline. As shown in Figure1, for image classification, our 50-layers network(HS-ResNet50) achieves 81.28% top-1 accuracy with competitive latency on ImageNet-1k dataset. It also outperforms most state-of-the-art models. The source code and models will be available on: https://github.com/PaddlePaddle/PaddleClas

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