Instructions to use LanguageBind/LanguageBind_Depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LanguageBind/LanguageBind_Depth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="LanguageBind/LanguageBind_Depth") 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_Depth", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b4855a0cb03d0157e2deba35bdadb50462fb3522026a3c5fe7631766b709fbc
- Size of remote file:
- 1.71 GB
- SHA256:
- 2213059043d6446335e6d65415a0891e9cd4b4889e1072b4b5d510268e4b31ae
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