ResNeSt provides a PyTorch implementation of Split-Attention Networks, a ResNet architecture variant that introduces channel-attention within residual blocks. The model serves as a backbone for various computer vision tasks including object detection, instance segmentation, semantic segmentation, and panoptic segmentation. It includes integration with Detectron2 and benchmarks on COCO and ADE20K datasets.