Instructions to use TencentARC/QA-CLIP-ViT-B-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TencentARC/QA-CLIP-ViT-B-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="TencentARC/QA-CLIP-ViT-B-16") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("TencentARC/QA-CLIP-ViT-B-16") model = AutoModelForZeroShotImageClassification.from_pretrained("TencentARC/QA-CLIP-ViT-B-16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27a7ccddd4fd8244d94c588db3c05c605fca1a9ac4e145cbfcd3f7a8beaaef03
- Size of remote file:
- 753 MB
- SHA256:
- 906a846b5169926337dc74f392553deb9759cab438ff15083ec8e09e3517d2e4
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