This notebook demonstrates how to fine-tune SigLIP 2, 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.
| Notebook Name | Description | Notebook Link |
|---|---|---|
| notebook-siglip2-finetune-type1 | Train/Test Splits | ⬇️Download |
| notebook-siglip2-finetune-type2 | Only Train Split | ⬇️Download |
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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.
last updated : jul 2025
| Type 1: Train/Test Splits | Type 2: Only Train Split |
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| Platform | Link |
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| Huggingface Blog | Blog |
| GitHub Repository |  |