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Found 6 papers, 5 papers with code
What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims
We successfully test 3 of these claims through a quantitative analysis, and directly compare one with traditional software.
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation.
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements.
PeaTMOSS: Mining Pre-Trained Models in Open-Source Software
Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks.
Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision
We describe this process as deep learning model reengineering.
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse.
