  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.
