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:
- c5f2e8eac30225ce3e73e9ddd23dc95d0b458baf520f9b4d835f959a2be8dc6f
- Size of remote file:
- 1.71 GB
- SHA256:
- 61469bebce081eb4552a3d5184172995677f8a5b9a40f59e3fed8c8a509d6b9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.