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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- notebook |
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- colab |
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- siglip2 |
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- image-to-text |
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--- |
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> **Finetune SigLIP2 Image Classification** |
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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. |
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| Notebook Name | Description | Notebook Link | |
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|-------------------------------------|--------------------------------------------------|----------------| |
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| notebook-siglip2-finetune-type1 | Train/Test Splits | [Download](https://huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb) | |
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| notebook-siglip2-finetune-type2 | Only Train Split | [Download](https://huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/2.SigLIP2_Finetune_ImageClassification_OnlyTrain_Splits.ipynb) | |
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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. |
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``` |
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last updated : jul 2025 |
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``` |
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| **Type 1: Train/Test Splits** | **Type 2: Only Train Split** | |
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|------------------------------|------------------------------| |
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|  |  | |
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--- |
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| Platform | Link | |
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|----------|------| |
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| Huggingface Blog | [](https://huggingface.co/blog/prithivMLmods/siglip2-finetune-image-classification) | |
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| GitHub Repository | [](https://github.com/PRITHIVSAKTHIUR/FineTuning-SigLIP-2) | |
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