SigLIP2用于航空火灾风险分类
SigLIP2 for aerial fire risk classification
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中文总结 AI 辅助
本研究评估预训练SigLIP2编码器在航空影像七类火灾风险分类中的迁移，通过可复现划分和全适应方法，在验证集上达到63.05%准确率，为后续评估提供框架。
中文摘要 AI 辅助
我们研究了预训练的SigLIP2图像编码器在航空影像七类火灾风险分类中的迁移。我们引入了公开FireRisk训练镜像的可复现划分，以及记录数据来源、预处理和模型选择的实现。两次初始运行比较了冻结编码器探针与全模型适应。在验证划分上，全适应达到63.05%的准确率和58.94%的宏F1，而探针分别为55.95%和50.19%。两次运行均使用一个训练种子，并在同一验证划分上选择检查点。这些开发结果支持对SigLIP2的进一步评估，但并未在独立测试集或未见区域上建立性能。随附代码为重复实验和与其他视觉编码器的比较提供了通用框架。
英文摘要
We examine the transfer of a pretrained SigLIP2 image encoder to seven class fire risk classification from aerial imagery. We introduce a reproducible partition of the public FireRisk training mirror and an implementation that records data provenance, preprocessing and model selection. Two initial runs compare a frozen encoder probe with full model adaptation. On the validation partition, full adaptation reaches 63.05% accuracy and 58.94% macro F1, compared with 55.95% and 50.19% for the probe. Both runs use one training seed and select their checkpoint on the same validation partition. These development results support further evaluation of SigLIP2 but do not establish performance on an independent test set or unseen regions. The accompanying code provides a common framework for repeated experiments and comparisons with additional visual encoders.
发表机构
- Marmara University（马尔马拉大学）
机构由 AI 辅助整理，请以论文原文为准。
