ADE20K Dataset

The authors of the ADE20K dataset address the significant challenge of scene parsing, encompassing the recognition and segmentation of objects and stuff within images, a vital task in the domain of computer vision. Despite the efforts made by the research community to gather data, there remains a scarcity of image datasets that comprehensively cover a broad spectrum of scenes and object categories, along with detailed and dense annotations suitable for scene parsing. To fill this void, the authors introduce the ADE20K dataset. This dataset features diverse annotations that span scenes, objects, parts of objects, and, intriguingly, even parts of parts. In order to facilitate benchmarking for scene parsing, the ADE20K dataset includes 150 object and stuff classes, and various segmentation baseline models undergo evaluation using this benchmark. You can access the hierarchy of classes on the official website of the dataset.

ADE20K数据集的制作者针对场景解析这一计算机视觉领域的重大挑战进行了深入研究，该挑战涵盖了图像中对象及事物的识别与分割。尽管研究界已做出努力以收集数据，但仍存在大量图像数据集未能全面涵盖广泛场景和对象类别，以及适合场景解析的详尽而密集的注释。为了填补这一空白，作者们推出了ADE20K数据集。该数据集拥有涵盖场景、对象、物体部分以及引人入胜的部分之部分的多样注释。为了促进场景解析的基准测试，ADE20K数据集包含了150个对象及物质类别，并使用这一基准对多种分割基线模型进行了评估。您可以在数据集的官方网站上查阅类别的层次结构。

搜集汇总

背景与挑战

背景概述

ADE20K数据集是一个2019年发布的计算机视觉数据集，主要用于实例分割、语义分割和物体检测任务，特别适用于场景解析和机器人领域。该数据集包含27,574张图像，标注了超过71万个对象实例，涵盖3,579个类别，包括墙、天空、地板等常见场景元素，并提供像素级实例分割标注，支持对象和部件的层次结构分析。其特点是开放类别标注，不预设固定对象列表，旨在促进全面的场景理解。