🚀 微调图像描述模型

这是一个经过微调的BLIP模型，用于对零售产品图像进行视觉问答。该模型在自定义数据集上进行了微调，这些数据集包含来自在线零售平台的图像，并配有产品描述注释。

这个实验性模型可用于回答零售行业产品图像相关的问题。产品元数据丰富、验证人工生成的产品描述等都是可能的应用场景。

🚀 快速开始

模型信息

模型预测示例

输入图像	预测结果
	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

💻 使用示例

基础用法

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))

📚 详细文档

BibTex和引用信息

@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}
}