Dhaman Manikanth
Dhaman09
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AI & ML interests
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Recent Activity
replied to
ccocks-deca's
post
1 day ago
Something big* coming...
big = biggest in the world
reacted
to
prithivMLmods's
post
with 😎
30 days ago
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.
reacted
to
prithivMLmods's
post
with 🤗
30 days ago
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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