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.

翻译：本研究为图像分类中的无源无监督领域自适应（SF-UDA）提供了一个全面的基准框架，旨在严格实证理解SF-UDA方法中多个关键设计因素间的复杂关系。我们实证检验了多种SF-UDA技术，评估了其在不同数据集间的一致性、对特定超参数的敏感性，以及在不同骨干架构系列中的适用性。此外，我们详尽评估了预训练数据集和策略，特别关注监督与自监督方法，以及源域微调的影响。我们的分析还揭示了现有基准实践中的不足之处，引导SF-UDA研究向更有效且通用的方向推进。研究强调了骨干架构和预训练数据集选择对SF-UDA性能的重要性，为领域提供了关键参考与洞见。最后，我们公开了实验框架的源代码，便于构建、训练和测试SF-UDA方法，支持系统性的大规模实验分析，为这一领域的后续研究提供助力。