Instructions to use mulsi/fruit-vegetable-clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mulsi/fruit-vegetable-clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mulsi/fruit-vegetable-clip-vit-base-patch32") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("mulsi/fruit-vegetable-clip-vit-base-patch32") model = AutoModelForImageClassification.from_pretrained("mulsi/fruit-vegetable-clip-vit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from mulsi/fruit-vegetable-clip-vit-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 350 MB
-
https://huggingface.co/mulsi/fruit-vegetable-clip-vit-base-patch32/resolve/main/model.safetensors
- Command line
-
hf download hf://mulsi/fruit-vegetable-clip-vit-base-patch32/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/mulsi/fruit-vegetable-clip-vit-base-patch32/resolve/main/model.safetensors
350 MB
- Xet hash:
- d7d613caac64abf8177487f14238cbd1b952f0d750fbc271e5858e95f28ad36c
- Size of remote file:
- 350 MB
- SHA256:
- c6b663048ea00325f2b500f0f20051840fd71c20642cf97a6645e920685fe560
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