Abstract
With pre-trained large models and their associated fine-tuning paradigms being constantly applied in deep learning, the performance of large models achieves a dramatic boost, mostly owing to the improvements on both data quantity and quality. Next-generation synchrotron light sources offer ultra-bright and highly coherent X-rays, which are becoming one of the largest data sources for scientific experiments. As one of the most data-intensive scanning-based imaging methodologies, ptychography produces an immense amount of data, making the adoption of large deep learning models possible. Here, we introduce and refine the architecture of a neural network model to improve the reconstruction performance, through fine-tuning large pre-trained model using a variety of datasets. The pre-trained model exhibits remarkable generalization capability, while the fine-tuning strategy enhances the reconstruction quality. We anticipate this work will contribute to the advancement of deep learning methods in ptychography, as well as in broader coherent diffraction imaging methodologies in future.