The Pyramid Scene Parsing Network (PSPNet) is proposed to address the challenges of scene parsing in open-vocabulary and diverse scenes. The method leverages global context information through a pyramid pooling module, which aggregates context from different regions to enhance scene understanding. PSPNet is designed to provide a superior framework for pixel-level prediction, achieving state-of-the-art performance on multiple benchmarks, including ImageNet scene parsing challenge 2016, PASCAL VOC 2012, and Cityscapes. It achieves mIoU accuracy of 85.4% on PASCAL VOC 2012 and 80.2% on Cityscapes. The network is based on a deep residual network with a pyramid pooling module that captures global context information. The method also incorporates a deeply supervised loss to optimize the network. PSPNet outperforms existing methods in terms of accuracy and performance on various datasets. The network is evaluated on multiple datasets and shows significant improvements in scene parsing tasks. The method is effective in handling complex scenes with diverse objects and stuff, and it provides more accurate and detailed results compared to existing methods. The proposed approach is a promising direction for pixel-level prediction tasks and may benefit other computer vision tasks such as stereo matching, optical flow, and depth estimation.The Pyramid Scene Parsing Network (PSPNet) is proposed to address the challenges of scene parsing in open-vocabulary and diverse scenes. The method leverages global context information through a pyramid pooling module, which aggregates context from different regions to enhance scene understanding. PSPNet is designed to provide a superior framework for pixel-level prediction, achieving state-of-the-art performance on multiple benchmarks, including ImageNet scene parsing challenge 2016, PASCAL VOC 2012, and Cityscapes. It achieves mIoU accuracy of 85.4% on PASCAL VOC 2012 and 80.2% on Cityscapes. The network is based on a deep residual network with a pyramid pooling module that captures global context information. The method also incorporates a deeply supervised loss to optimize the network. PSPNet outperforms existing methods in terms of accuracy and performance on various datasets. The network is evaluated on multiple datasets and shows significant improvements in scene parsing tasks. The method is effective in handling complex scenes with diverse objects and stuff, and it provides more accurate and detailed results compared to existing methods. The proposed approach is a promising direction for pixel-level prediction tasks and may benefit other computer vision tasks such as stereo matching, optical flow, and depth estimation.