This model is fintuned on instruction dataset using SalesForce/blip-imagecaptioning-base model.
Usage:
from transformers import BlipProcessor, BlipForConditionalGeneration
import torch
from PIL import Image
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
if processor.tokenizer.eos_token is None:
processor.tokenizer.eos_token = '<|eos|>'
model = BlipForConditionalGeneration.from_pretrained("prasanna2003/Instruct-blip-v2")
image = Image.open('file_name.jpg').convert('RGB')
prompt = """Instruction: Answer the following input according to the image.
Input: Describe this image.
output: """
inputs = processor(image, prompt, return_tensors="pt")
output = model.generate(**inputs, max_length=100)
print(tokenizer.decode(output[0]))
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