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from transformers import CLIPModel, AutoProcessor
from PIL import Image
model = CLIPModel.from_pretrained(
"openai/clip-vit-large-patch14"
)
processor = AutoProcessor.from_pretrained(
"openai/clip-vit-large-patch14"
)
labels = ["a photo of a cat", "a photo of a dog"]
def classify_image(image_path):
"""
Perform zero-shot classification on a single image.
Args:
image_path (str): Path to the image.
Returns:
dict: Classification probabilities for the image.
"""
image = Image.open(image_path) # Open the image
# Preprocess the image and labels
inputs = processor(
text=labels,
images=image,
return_tensors="pt",
padding=True
)
# Perform inference using the CLIP model
outputs = model(**inputs)
probs = outputs.logits_per_image.softmax(dim=1)[0] # Calculate probabilities
# Return results as a dictionary with label and probability pairs
return {labels[i]: probs[i].item() for i in range(len(labels))}
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