Instructions to use arampacha/clip-rsicd-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arampacha/clip-rsicd-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="arampacha/clip-rsicd-v5") 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("arampacha/clip-rsicd-v5") model = AutoModelForZeroShotImageClassification.from_pretrained("arampacha/clip-rsicd-v5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75375057d6b908ecdd973cce2e29d0b78e3c4141bbd4139e23e1fe8b193f6266
- Size of remote file:
- 605 MB
- SHA256:
- 4ce492ecc4ad14e9288efcf05d696ba73bd73d3eca51fc6b460a627bf216ca0a
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