Instructions to use adasdimchom/clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adasdimchom/clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="adasdimchom/clip-vit-base-patch32") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("adasdimchom/clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("adasdimchom/clip-vit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload handler.py
ad35ed2 -
Duplicate from openai/clip-vit-base-patch32
Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>b101841