Jose et al. MLST 2025 - Code, Data and Models
收藏DaRUS2025-01-01 更新2026-04-16 收录
下载链接：
https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/DARUS-5512
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资源简介：
This dataset hosts the code, data and models needed for replication of the work mentioned in Jose et al. MLST 2025. In the paper, an ablation study is conducted to delineate the effects of domain randomisation parameters of synthetically generated training data on the segmentation accuracy. The best model is used to extract high-level statistics from soot filaments in an RQL-type model combustor to enhance the fundamental understanding soot formation, transport and oxidation. B. Jose, K. P. Geigle, F. Hampp, Domain-Randomised Instance-Segmentation Benchmark for Soot in PIV Images, submitted to Machine Learning: Science and Technology (2025)
提供机构：
Deutsches Zentrum für Luft- und Raumfahrt e. V.; University of Stuttgart
创建时间：
2025-01-01
