Upgraded the step-by-step notebook for fine-tuning SigLIP2 on domain-specific image classification tasks. The notebook supports both datasets with predefined train/test splits and those with only a train split, making it suitable for low-resource, custom, and real-world classification scenarios. 📢👉
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Upgraded the step-by-step notebook for fine-tuning SigLIP2 on domain-specific image classification tasks. The notebook supports both datasets with predefined train/test splits and those with only a train split, making it suitable for low-resource, custom, and real-world classification scenarios. 📢👉
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➺ FineTuning-SigLIP2-Notebook : 
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➺ FineTuning-SigLIP2-Notebook : 
https://huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook
prithivMLmods/FineTuning-SigLIP2-Notebook
model
resource
https://huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook
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➺ GitHub : 
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➺ GitHub : 
https://github.com/PRITHIVSAKTHIUR/FineTuning-SigLIP-2
https://github.com/PRITHIVSAKTHIUR/FineTuning-SigLIP-2
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➺ In the first, datasets include predefined train and test splits, enabling conventional supervised learning and generalization evaluation : 
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➺ In the first, datasets include predefined train and test splits, enabling conventional supervised learning and generalization evaluation : 
https://colab.research.google.com/#fileId=https%3A//huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb
prithivMLmods/FineTuning-SigLIP2-Notebook
model
resource
https://colab.research.google.com/#fileId=https%3A//huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb
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text
 (.ipynb)
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➺ 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 : 
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➺ 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 : 
https://colab.research.google.com/#fileId=https%3A//huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/2.SigLIP2_Finetune_ImageClassification_OnlyTrain_Splits.ipynb
prithivMLmods/FineTuning-SigLIP2-Notebook
model
resource
https://colab.research.google.com/#fileId=https%3A//huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/2.SigLIP2_Finetune_ImageClassification_OnlyTrain_Splits.ipynb
 (.ipynb)
text
 (.ipynb)
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This flexibility supports experimentation in constrained or domain-specific settings, where standard test annotations may not exist.
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This flexibility supports experimentation in constrained or domain-specific settings, where standard test annotations may not exist.
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Upgraded the step-by-step notebook for fine-tuning SigLIP2 on domain-specific image classification tasks. The notebook supports both datasets with predefined train/test splits and those with only a train split, making it suitable for low-resource, custom, and real-world classification scenarios. 📢👉

➺ FineTuning-SigLIP2-Notebook : https://huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook

➺ GitHub : https://github.com/PRITHIVSAKTHIUR/FineTuning-SigLIP-2

➺ In the first, datasets include predefined train and test splits, enabling conventional supervised learning and generalization evaluation : https://colab.research.google.com/#fileId=https%3A//huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb (.ipynb)

➺ 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 : https://colab.research.google.com/#fileId=https%3A//huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/2.SigLIP2_Finetune_ImageClassification_OnlyTrain_Splits.ipynb (.ipynb)

This flexibility supports experimentation in constrained or domain-specific settings, where standard test annotations may not exist.

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