MiMVP deep learning platform: streamlining deep learning model development workflow for machine vision tasks

Nur Afiqah Kamarudin; Muhammad Syukri Mohd Talib; Phooi Yee Lau; Zarina Hussin; Shang Li Yuen; Muhammad Hafiz Samsuri; Chin Wee Wong; Hock Woon Hon

doi:10.1117/12.3017907

2 May 2024 MiMVP deep learning platform: streamlining deep learning model development workflow for machine vision tasks

Nur Afiqah Kamarudin, Muhammad Syukri Mohd Talib, Phooi Yee Lau, Zarina Hussin, Shang Li Yuen, Muhammad Hafiz Samsuri, Chin Wee Wong, Hock Woon Hon

Author Affiliations +

Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 131640B (2024) https://doi.org/10.1117/12.3017907
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia

Abstract

Deep learning (DL) for machine vision tasks has seen enormous growth and success in recent years. However, the complexity of the DL model development workflow and the prevalence of code-based solutions which are powerful for research but have limited scalability, poses a challenge in managing a large number of DL projects and datasets. In this paper, we propose an integrated platform, named MIMOS Machine Vision Package (MiMVP) Deep Learning. The MiMVP could provide a single, desktop-based visual interface to manage DL projects and guide the user through the workflow of developing and training DL models, from preparing data, training and tuning model, and comparing and analyzing results. By streamlining the DL workflow, this platform can enhance the efficiency of DL model development .

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nur Afiqah Kamarudin, Muhammad Syukri Mohd Talib, Phooi Yee Lau, Zarina Hussin, Shang Li Yuen, Muhammad Hafiz Samsuri, Chin Wee Wong, and Hock Woon Hon "MiMVP deep learning platform: streamlining deep learning model development workflow for machine vision tasks", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 131640B (2 May 2024); https://doi.org/10.1117/12.3017907

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