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:
- 9708abb5224a6b39c2421b9ff8640420f1e4f3f7f3d833f794d0088f4ef68dfb
- Size of remote file:
- 90.1 kB
- SHA256:
- cf88a89fc8080e7a0e1df00b1d2d7d16580270755e81e6a89b7b1b8e47f981d0
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