IndustReal Dataset of Egocentric Videos for Procedure Understanding

The multi-modal IndustReal dataset, accompanying our publication "IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Setting". Check out our GitHub for additional details and read-me files. Unlike currently available datasets, IndustReal contains procedural errors (such as omissions) as well as execution errors. A significant part of these errors are exclusively present in the validation and test sets, making IndustReal suitable to evaluate robustness of algorithms to new, unseen mistakes. Additionally, to encourage reproducibility and allow for scalable approaches trained on synthetic data, the 3D models of all parts are publicly available.多模态IndustReal数据集配套于我们发表的学术论文《IndustReal：面向类工业场景第一人称视频中处理执行错误的流程步骤识别数据集》。如需获取更多细节与说明文档，请访问我们的GitHub仓库。与当前已公开的数据集不同，IndustReal包含流程类错误（如操作遗漏）与执行类错误。其中大量错误仅存在于验证集与测试集中，使得IndustReal能够有效评估算法面对全新、未见过的错误场景时的鲁棒性。此外，为推动研究可复现性并支持基于合成数据训练的可扩展算法，该数据集所有组件的3D模型均已公开。