Instructions to use humane-lab/CFT-CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use humane-lab/CFT-CLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="humane-lab/CFT-CLIP") 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("humane-lab/CFT-CLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("humane-lab/CFT-CLIP") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a38bd407aceac6eed2188302f3866482d8a1d1f5f2523186d5c4824bc339b34
- Size of remote file:
- 1.71 GB
- SHA256:
- ed9a08a6b94048d026515774300e6744d23cfbfb67a6ef45e57acf4ce86ce4fa
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