Image Classification
Transformers
Safetensors
English
siglip
Digits
Mnist
SigLIP2
0-t0-9
Number-Classification
Instructions to use prithivMLmods/Mnist-Digits-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Mnist-Digits-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Mnist-Digits-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Mnist-Digits-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Mnist-Digits-SigLIP2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# **Mnist-Digits-SigLIP2**
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- **Optical Character Recognition (OCR):** Digit recognition for various documents.
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- **Banking & Finance:** Automated check processing.
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- **Education & Learning:** AI-powered handwriting assessment.
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- **Embedded Systems:** Handwriting input in smart devices.
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# **Mnist-Digits-SigLIP2**
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- **Optical Character Recognition (OCR):** Digit recognition for various documents.
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- **Banking & Finance:** Automated check processing.
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- **Education & Learning:** AI-powered handwriting assessment.
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- **Embedded Systems:** Handwriting input in smart devices.
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