  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
