Instructions to use hf-internal-testing/tiny-random-SiglipForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SiglipForImageClassification") 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("hf-internal-testing/tiny-random-SiglipForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 814b9d135d41b9e203a589ac42cc9d859dcd3ae7d934bdf2f23bc2d7fa06cb0a
- Size of remote file:
- 118 kB
- SHA256:
- 9e929649f1babdcc25d9e28360cbc84792766c546c4b3845137be0bd2717c914
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