HyperRPS: An Efficient Multi-Fidelity Hyper-Parameter Tuning Framework for Rock Particle Segmentation

Rock particle segmentation is essential for identifying thin sections accurately, yet tuning deep learning models for this task is challenging. Existing studies have attempted auto-tuning techniques but face three limitations, including limited search space, inefficient search, and high demand for storage space. In this paper, we introduce HyperRPS, a novel multi-fidelity hyper-parameter tuning framework tailored for rock particle segmentation, which addresses these issues simultaneously. First, by augmenting the search space with diverse hyper-parameters, we unleash the potential of the segmentation model. Second, to accelerate the hyper-parameter search, we propose a tuning algorithm leveraging multi-fidelity surrogate fitting and time constraint modeling. Third, we develop a storage management technique to reduce the space requirements for asynchronous scheduling. Experimental results on both public and rock particle segmentation datasets demonstrate HyperRPS’s superior performance. Specifically, on the rock particle segmentation dataset, HyperRPS achieves notable improvements of 8.26% and 2.78% on average precision when tuning Mask2Former (Swin-B) and Mask2Former (Swin-L), respectively, outperforming existing state-of-the-art methods.