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PRITHIVSAKTHIUR
/ run_finetune_vit.py
Last active
October 6, 2025 11:28
A Comprehensive Single-Shot Fine-Tuning Code Snippet for ViT (vit-base-patch16-224-in21k) on Domain-Specific Downstream Image Classification Tasks.
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| # Fine-Tuning ViT for Image Classification | Script prepared by: hf.co/prithivMLmods | |
| # | |
| # Dataset with Train & Test Splits | |
| # | |
| # In this configuration, the dataset is already organized into separate training and testing splits. This setup is ideal for straightforward supervised learning workflows. | |
| # | |
| # Training Phase: | |
| # The model is fine-tuned exclusively on the train split, where each image is paired with its corresponding class label. | |
| # | |
| # Evaluation Phase: |
PRITHIVSAKTHIUR
/ run_finetune_siglip2.py
Last active
October 6, 2025 11:27
A Comprehensive Single-Shot Fine-Tuning Code Snippet for SigLIP2 on Domain-Specific Downstream Image Classification Tasks.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Fine-Tuning SigLIP2 for Image Classification | Script prepared by: hf.co/prithivMLmods | |
| # | |
| # Dataset with Train & Test Splits | |
| # | |
| # In this configuration, the dataset is already organized into separate training and testing splits. This setup is ideal for straightforward supervised learning workflows. | |
| # | |
| # Training Phase: | |
| # The model is fine-tuned exclusively on the train split, where each image is paired with its corresponding class label. | |
| # | |
| # Evaluation Phase: |