Instructions to use LanguageBind/LanguageBind_Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LanguageBind/LanguageBind_Image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="LanguageBind/LanguageBind_Image") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModelForZeroShotImageClassification model = AutoModelForZeroShotImageClassification.from_pretrained("LanguageBind/LanguageBind_Image", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2980716682e1500ccb42bd99189dfc074888caacd257446acb406651276621b3
- Size of remote file:
- 1.71 GB
- SHA256:
- 99c9382819ef4021e9b2600f030f267d18cc4d5dad39928fdd83ed48887e94bd
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