Instructions to use tanganke/clip-vit-base-patch32_oxford-iiit-pet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanganke/clip-vit-base-patch32_oxford-iiit-pet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tanganke/clip-vit-base-patch32_oxford-iiit-pet")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tanganke/clip-vit-base-patch32_oxford-iiit-pet") model = AutoModel.from_pretrained("tanganke/clip-vit-base-patch32_oxford-iiit-pet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9e03aa40ef0aa9a34808790c23c2359b9bb8e434ac2f4ef34176485756894ea
- Size of remote file:
- 196 kB
- SHA256:
- 1a1360573363a0d4e89d1704e5e79d890012f7ebe247b9a2d34d27f18b10eb0e
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