readme: ---
  pipeline_tags: 'other'
  tags:
    - image-text-matching
  languages:
    - en
  license: bsd-3-clause
  ---
  
  # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
  
  Model card for BLIP trained on image-text matching - large architecture (with ViT large backbone) trained on COCO dataset.
  
  | ![BLIP.gif](https://s3.amazonaws.com/moonup/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) |
  |:--:|
  | <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|
  
  ## TL;DR
  
  Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:
  
  *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
  
  ## Usage
  
  You can use this model for conditional and un-conditional image captioning
  
  ### Using the Pytorch model
  
  #### Running the model on CPU
  
  <details>
  <summary> Click to expand </summary>
  
  ```python
  import requests
  from PIL import Image
  from transformers import BlipProcessor, BlipForImageTextRetrieval
  
  processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-large-coco")
  model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-large-coco")
  
  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')
  
  question = "A woman and a dog sitting together in a beach."
  inputs = processor(raw_image, question, return_tensors="pt")
  
  itm_scores = model(**inputs)[0]
  cosine_score = model(**inputs, use_itm_head=False)[0]
  ```
  </details>
  
  #### Running the model on GPU
  
  ##### In full precision 
  
  <details>
  <summary> Click to expand </summary>
  
  ```python
  import requests
  from PIL import Image
  from transformers import BlipProcessor, BlipForImageTextRetrieval
  
  processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-large-coco")
  model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-large-coco").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')
  
  question = "A woman and a dog sitting together in a beach."
  inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
  
  itm_scores = model(**inputs)[0]
  cosine_score = model(**inputs, use_itm_head=False)[0]
  ```
  </details>
  
  ##### In half precision (`float16`)
  
  <details>
  <summary> Click to expand </summary>
  
  ```python
  import torch
  import requests
  from PIL import Image
  from transformers import BlipProcessor, BlipForImageTextRetrieval
  
  processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-large-coco")
  model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-large-coco", 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')
  
  question = "A woman and a dog sitting together in a beach."
  inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
  
  itm_scores = model(**inputs)[0]
  cosine_score = model(**inputs, use_itm_head=False)[0]
  ```
  </details>
  
  ## 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}
  }
  ```