  This study provides a comprehensive benchmark framework for Source-Free
Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to
achieve a rigorous empirical understanding of the complex relationships between
multiple key design factors in SF-UDA methods. The study empirically examines a
diverse set of SF-UDA techniques, assessing their consistency across datasets,
sensitivity to specific hyperparameters, and applicability across different
families of backbone architectures. Moreover, it exhaustively evaluates
pre-training datasets and strategies, particularly focusing on both supervised
and self-supervised methods, as well as the impact of fine-tuning on the source
domain. Our analysis also highlights gaps in existing benchmark practices,
guiding SF-UDA research towards more effective and general approaches. It
emphasizes the importance of backbone architecture and pre-training dataset
selection on SF-UDA performance, serving as an essential reference and
providing key insights. Lastly, we release the source code of our experimental
framework. This facilitates the construction, training, and testing of SF-UDA
methods, enabling systematic large-scale experimental analysis and supporting
further research efforts in this field.
