  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.
