Instructions to use waelhasan/clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waelhasan/clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="waelhasan/clip-vit-base-patch32") 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("waelhasan/clip-vit-base-patch32") model = AutoModelForImageClassification.from_pretrained("waelhasan/clip-vit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fb60e78287210c48cc86dd166a9b8f966a20af69ca7edc57e878894a09a0ec8
- Size of remote file:
- 5.14 kB
- SHA256:
- 78fc9d89ad8e328aa5483a5722ac6d1da58545022e2e032cdad1a54a13ba7120
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