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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license: apache-2.0
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license: apache-2.0
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---
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```py
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Classification Report:
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precision recall f1-score support
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0 0.9988 0.9959 0.9974 5923
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1 0.9987 0.9918 0.9952 6742
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2 0.9918 0.9943 0.9930 5958
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3 0.9975 0.9938 0.9957 6131
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4 0.9892 0.9882 0.9887 5842
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5 0.9859 0.9937 0.9898 5421
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6 0.9936 0.9939 0.9937 5918
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7 0.9856 0.9943 0.9899 6265
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8 0.9932 0.9921 0.9926 5851
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9 0.9926 0.9897 0.9912 5949
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accuracy 0.9928 60000
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macro avg 0.9927 0.9928 0.9927 60000
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weighted avg 0.9928 0.9928 0.9928 60000
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```
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