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Finetune-SigLIP2-Image-Classification/Finetune_SigLIP2_Image_Classification_README.md

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+**Finetune SigLIP2 Image Classification**
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+This notebook demonstrates how to fine-tune [SigLIP 2](https://huggingface.co/google/siglip2-base-patch16-224), a robust multilingual vision-language model, for single-label image classification tasks. The fine-tuning process incorporates advanced techniques such as captioning-based pretraining, self-distillation, and masked prediction, unified within a streamlined training pipeline. The workflow supports datasets in both structured and unstructured forms, making it adaptable to various domains and resource levels.
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+The notebook outlines two data handling scenarios. In the first, datasets include predefined train and test splits, enabling conventional supervised learning and generalization evaluation. In the second scenario, only a training split is available; in such cases, the training set is either partially reserved for validation or reused entirely for evaluation. This flexibility supports experimentation in constrained or domain-specific settings, where standard test annotations may not exist.