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.<br>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数据集与我们的学术论文《IndustReal：面向类工业场景下第一人称视角视频的流程步骤识别及执行错误处理数据集》配套发布。如需获取更多细节信息与说明文档，请访问我们的GitHub仓库。与当前已公开的各类数据集不同，IndustReal数据集包含流程错误（如操作遗漏）与执行错误两类异常场景。其中大量异常错误仅存在于验证集与测试集当中，这使得IndustReal数据集能够有效评估算法对全新未见过的错误场景的鲁棒性。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研究，该数据集所有部件的3D模型均已对外公开。