ResNeSt：注意力分散网络

众所周知，要素图注意和多路径表示对于视觉识别非常重要。在本文中，我们提出了一种模块化的体系结构，该体系结构将通道注意应用于不同的网络分支，以利用它们在捕获跨功能交互和学习各种表示形式方面的成功。..

ResNeSt: Split-Attention Networks

It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations.Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge. The source code for complete system and pretrained models are publicly available.