  High-fidelity datasets play a pivotal role in imbuing simulators with
realism, enabling the benchmarking of various state-of-the-art deep inference
models. These models are particularly instrumental in tasks such as semantic
segmentation, classification, and localization. This study showcases the
efficacy of a customized manufacturing dataset comprising 60 classes in the
creation of a high-fidelity digital twin of a robotic manipulation environment.
By leveraging the concept of transfer learning, different 6D pose estimation
models are trained within the simulated environment using domain randomization
and subsequently tested on real-world objects to assess domain adaptation. To
ascertain the effectiveness and realism of the created data-set, pose accuracy
and mean absolute error (MAE) metrics are reported to quantify the model2real
gap.
