Post
5023
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 : 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 : prithivMLmods/FineTuning-SigLIP2-Notebook (.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 : prithivMLmods/FineTuning-SigLIP2-Notebook (.ipynb)
This flexibility supports experimentation in constrained or domain-specific settings, where standard test annotations may not exist.
➺ FineTuning-SigLIP2-Notebook : 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 : prithivMLmods/FineTuning-SigLIP2-Notebook (.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 : prithivMLmods/FineTuning-SigLIP2-Notebook (.ipynb)
This flexibility supports experimentation in constrained or domain-specific settings, where standard test annotations may not exist.