Analysis of AI Model Hub

• Published in: International Journal of Advanced Culture Technology(IJACT) Vol. 11; no. 4; pp. 442 - 448
• Main Author: Yo-Seob Lee
• Format: Journal Article
• Language: English
• Published: 국제문화기술진흥원 31.12.2023
• Subjects: 과학기술학; Machine Learning Model; TensorFlow Hub; Model Hub; Kaggle; HuggingFace; AI Model
• ISSN: 2288-7202; 2288-7318
• DOI: 10.17703/IJACT.2023.11.4.442

Artificial Intelligence (AI) technology has recently grown explosively and is being used in a variety of application fields. Accordingly, the number of AI models is rapidly increasing. AI models are adapted and developed to fit a variety of data types, tasks, and environments, and the variety and volume of models continues to grow. The need to share models and collaborate within the AI community is becoming increasingly important. Collaboration is essential for AI models to be shared and improved publicly and used in a variety of applications. Therefore, with the advancement of AI, the introduction of Model Hub has become more important, improving the sharing, reuse, and collaboration of AI models and increasing the utilization of AI technology. In this paper, we collect data on the model hub and analyze the characteristics of the model hub and the AI models provided. The results of this research can be of great help in developing various multimodal AI models in the future, utilizing AI models in various fields, and building services by fusing various AI models.