This work presents the Industrial Hand Action Dataset V1, an industrial assembly dataset consisting of 12 classes with 459,180 images in the basic version and 2,295,900 images after spatial augmentation. Compared to other freely available datasets tested, it has an above-average duration and, in addition, meets the technical and legal requirements for industrial assembly lines. Furthermore, the dataset contains occlusions, hand-object interaction, and various fine-grained human hand actions for industrial assembly tasks that were not found in combination in examined datasets. The recorded ground truth assembly classes were selected after extensive observation of real-world use cases. A Gated Transformer Network, a state-of-the-art model from the transformer domain was adapted, and proved with a test accuracy of 86.25% before hyperparameter tuning by 18,269,959 trainable parameters, that it is possible to train sequential deep learning models with this dataset.

翻译：本文介绍了工业手部动作数据集V1，这是一个包含12个类别的工业装配数据集，基础版本包含459,180张图像，经过空间增强后达到2,295,900张图像。与测试的其他公开可用数据集相比，该数据集具有高于平均水平的时长，同时满足工业装配线的技术和法律要求。此外，该数据集包含遮挡、手-物体交互以及多种面向工业装配任务的细粒度人手动作，这些特征在现有数据集中未发现组合存在。记录的基准真实装配类别是在对实际应用案例进行广泛观察后选择的。我们改进了Transformer领域的先进模型——门控Transformer网络，并通过18,269,959个可训练参数在超参数调优前达到86.25%的测试准确率，证明利用该数据集训练序列深度学习模型是可行的。