  DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a
state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel
prediction problems in computer vision. DeepLab2 includes all our recently
developed DeepLab model variants with pretrained checkpoints as well as model
training and evaluation code, allowing the community to reproduce and further
improve upon the state-of-art systems. To showcase the effectiveness of
DeepLab2, our Panoptic-DeepLab employing Axial-SWideRNet as network backbone
achieves 68.0% PQ or 83.5% mIoU on Cityscaspes validation set, with only
single-scale inference and ImageNet-1K pretrained checkpoints. We hope that
publicly sharing our library could facilitate future research on dense pixel
labeling tasks and envision new applications of this technology. Code is made
publicly available at \url{https://github.com/google-research/deeplab2}.
