The ADE20K dataset is a densely annotated image dataset containing 25,000 images of complex everyday scenes with detailed pixel-level annotations for objects, parts, and stuff. It includes 19.5 instances and 10.5 object classes per image on average. The dataset was manually annotated by a single expert, resulting in high-quality and consistent annotations. It contains 3,169 class labels, including 2,693 object and stuff classes and 476 object part classes. The dataset is used to construct benchmarks for scene parsing and instance segmentation, and state-of-the-art models are re-implemented and open-sourced. The dataset also includes a large number of object and part classes, with 153 part classes in total. The dataset is compared with other datasets, showing it is more diverse and comprehensive. The dataset is used to evaluate the performance of semantic segmentation models, and the results show that a reasonably large batch size is crucial for achieving high performance. The dataset is also used to evaluate the performance of instance segmentation models, and the results show that instance segmentation is challenging for small objects. The dataset is used to evaluate the performance of scene parsing models, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene synthesis, and the results show that the scene parsing network can be used to synthesize new images. The dataset is used to evaluate the performance of hierarchical semantic segmentation, and the results show that the hierarchical structure of the dataset allows for more accurate segmentation. The dataset is used to evaluate the performance of automatic image content removal, and the results show that the scene parsing network can be used to automatically remove objects from images. The dataset is used to evaluate the performance of scene synthesis, and the results show that the scene parsing network can be used to synthesize new images. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information.The ADE20K dataset is a densely annotated image dataset containing 25,000 images of complex everyday scenes with detailed pixel-level annotations for objects, parts, and stuff. It includes 19.5 instances and 10.5 object classes per image on average. The dataset was manually annotated by a single expert, resulting in high-quality and consistent annotations. It contains 3,169 class labels, including 2,693 object and stuff classes and 476 object part classes. The dataset is used to construct benchmarks for scene parsing and instance segmentation, and state-of-the-art models are re-implemented and open-sourced. The dataset also includes a large number of object and part classes, with 153 part classes in total. The dataset is compared with other datasets, showing it is more diverse and comprehensive. The dataset is used to evaluate the performance of semantic segmentation models, and the results show that a reasonably large batch size is crucial for achieving high performance. The dataset is also used to evaluate the performance of instance segmentation models, and the results show that instance segmentation is challenging for small objects. The dataset is used to evaluate the performance of scene parsing models, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene synthesis, and the results show that the scene parsing network can be used to synthesize new images. The dataset is used to evaluate the performance of hierarchical semantic segmentation, and the results show that the hierarchical structure of the dataset allows for more accurate segmentation. The dataset is used to evaluate the performance of automatic image content removal, and the results show that the scene parsing network can be used to automatically remove objects from images. The dataset is used to evaluate the performance of scene synthesis, and the results show that the scene parsing network can be used to synthesize new images. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information. The dataset is used to evaluate the performance of scene parsing, and the results show that the performance of scene parsing can be improved by incorporating instance information.