Qhub Blip Image Captioning Finetuned

🚀 Fine-Tuned Image Captioning Model

This is a fine-tuned model based on BLIP, designed for visual answering on retail product images, offering practical solutions for the retail industry.

✨ Features

• This is a fine-tuned version of BLIP for visual answering on retail product images. It is finetuned on a custom dataset with images from an online retail platform and annotated with product descriptions.
• This experimental model can be used for answering questions on product images in the retail industry. Use cases include product meta data enrichment and validation of human-generated product descriptions.

📦 Model Information

💻 Usage Examples

Basic Usage

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

processor = BlipProcessor.from_pretrained("quadranttechnologies/qhub-blip-image-captioning-finetuned")
model = BlipForConditionalGeneration.from_pretrained("quadranttechnologies/qhub-blip-image-captioning-finetuned")

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))

📚 Sample Model Predictions

Input Image	Prediction
	kitchenaid artisann stand mixer
	a bottle of milk sitting on a counter
	dove sensitive skin lotion
	bread bag with blue plastic handl
	bush ' s best white beans

📄 License

The model is released under the apache-2.0 license.

📚 BibTex and Citation Info

@misc{https://doi.org/10.48550/arxiv.2201.12086,
  doi = {10.48550/ARXIV.2201.12086},
  
  url = {https://arxiv.org/abs/2201.12086},
  
  author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}