Instructions to use prithivMLmods/FineTuning-SigLIP2-Notebook with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/FineTuning-SigLIP2-Notebook with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/FineTuning-SigLIP2-Notebook") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/FineTuning-SigLIP2-Notebook", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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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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| 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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<div style="
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background: rgba(255, 193, 61, 0.15);
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padding: 16px;
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border-radius: 6px;
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border: 1px solid rgba(255, 165, 0, 0.3);
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margin: 16px 0;
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Finetune SigLIP2 Image Classification (Notebook)
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</div>
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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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| 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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