Instructions to use prithivMLmods/Fashion-Mnist-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Fashion-Mnist-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Fashion-Mnist-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/Fashion-Mnist-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Fashion-Mnist-SigLIP2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f12494514173df2e12702a95c46fc4585a2451e3114baeb5483e38cedff5dfc4
- Size of remote file:
- 1.06 kB
- SHA256:
- 8c636060e32831a2f047136ebca516c48b537dc20cc1ffb3fdc1b08dea5335af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.