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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d77333cc68582229a1359b7e320eac173865e9ad981e477243c2fb35b496482b
- Size of remote file:
- 605 MB
- SHA256:
- b4930e311cd9d9b655f56f2129648602252023450f05fb0e5310fb3ae3fc514a
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