Update README.md

README.md

@@ -23,12 +23,16 @@ Finetune SigLIP2 Image Classification (Notebook)
 
 
 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](https://huggingface.co/prithivMLmods/FineTuning-SigLIP2-Notebook/blob/main/Finetune-SigLIP2-Image-Classification/1.SigLIP2_Finetune_ImageClassification_TrainTest_Splits.ipynb) |
 | 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) |
 
+---
+
 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.
 
 ```