# Data-Link: High Fidelity Manufacturing Datasets for Model2Real Transfer under Industrial Settings Source: [Data-Link: High Fidelity Manufacturing Datasets for Model2Real Transfer under Industrial Settings]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:25:24.907Z Authors: Sunny Katyara, Mohammad Mujtahid, Court Edmondson Published: 2023-06-09T09:04:35.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()