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:
- 95389c5fa2e58510d924199bfb7e1f9fd505e2431d8ddbd03da9167dde83f73c
- Size of remote file:
- 90.1 kB
- SHA256:
- 2eb00587344d3e5b7fa0afd4b1a22a37fa43e94707962445d754056d3a2102f9
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