Blip Image Captioning Large Mocha

🚀 Mocha Checkpoint for BLIP-Large Model

This is the official checkpoint of the BLIP-Large model. It has been finetuned on MS-COCO using the MOCHa RL framework. The model is introduced in the paper Mitigating Open-Vocabulary Caption Hallucinations.

Project Page

🚀 Quick Start

You can use this model for conditional and un-conditional image captioning.

💻 Usage Examples

Basic Usage

This example shows how to run the model on CPU:

import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")
model = BlipForConditionalGeneration.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))

Advanced Usage

Running the model on GPU in full precision

import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")
model = BlipForConditionalGeneration.from_pretrained("moranyanuka/blip-image-captioning-large-mocha").to("cuda")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))

Running the model on GPU in half precision (float16)

import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")
model = BlipForConditionalGeneration.from_pretrained("moranyanuka/blip-image-captioning-large-mocha", torch_dtype=torch.float16).to("cuda")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and a dog on the beach

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> there is a woman and a dog on the beach at sunset

📄 License

This project is licensed under the MIT license.

📚 Documentation

BibTeX

@misc{benkish2024mitigating,
      title={Mitigating Open-Vocabulary Caption Hallucinations}, 
      author={Assaf Ben-Kish and Moran Yanuka and Morris Alper and Raja Giryes and Hadar Averbuch-Elor},
      year={2024},
      eprint={2312.03631},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}