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")
https://huggingface.co/mulsi/fruit-vegetable-clip-vit-base-patch32/resolve/main/merges.txt
hf download hf://mulsi/fruit-vegetable-clip-vit-base-patch32/merges.txt
curl -L -o merges.txt https://huggingface.co/mulsi/fruit-vegetable-clip-vit-base-patch32/resolve/main/merges.txt
