Instructions to use LanguageBind/LanguageBind_Thermal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LanguageBind/LanguageBind_Thermal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="LanguageBind/LanguageBind_Thermal") 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_Thermal", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 001f698d6c8254c31217dc6174b8a431fbf5c6f10827e5a25c2f3fc32d300f36
- Size of remote file:
- 1.71 GB
- SHA256:
- 56d84331cebe20f63a9b2a93a2217109554724cd1e8348ee4aafb383fc7e674f
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