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aeba2d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | from transformers import BlipProcessor, BlipForConditionalGeneration
import torch
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
def generate_caption(image):
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
out = model.generate(**inputs, max_length=50, num_beams=5, early_stopping=True, no_repeat_ngram_size=2)
caption = processor.decode(out[0], skip_special_tokens=True)
return caption |