Instructions to use hf-internal-testing/tiny-random-CLIPForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CLIPForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-CLIPForImageClassification") 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-CLIPForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-CLIPForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f10bfbe9e764b1f66d8e51a9b5c773a0ead8fc27675d0e2e74b1cf6c81533572
- Size of remote file:
- 90.1 kB
- SHA256:
- 2701a1462ef3ce8f6770ab1b6b54a70a8e4c33d3407ba70182ef1bde676de1b3
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