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
- Xet hash:
- 4958e6791ab265dae8f27bfa001a0e74321486267741e4e02115cacd559adea6
- Size of remote file:
- 5.3 kB
- SHA256:
- fa26e4f32f2c0dea4691f20df0231475a0a2fa288f905d660d89b3db9b37c2c3
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