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arXiv Computer Vision By Yunus Serhat B{\i}\c{c}ak\c{c}{\i}

SigLIP2 for aerial fire risk classification

Read the original on arXiv Computer Vision →

The paper presents an evaluation of the SigLIP2 image encoder for classifying fire risk from aerial images into seven categories. It introduces a reproducible split of the public FireRisk dataset and an implementation that tracks data provenance, preprocessing, and model selection. Two experiments—one with a frozen encoder probe and one with full model adaptation—show that full adaptation achieves 63.05% accuracy and 58.94% macro F1 on the validation set, outperforming the probe’s 55.95% accuracy and 50.19% macro F1.

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