Blip Image Captioning Base Football Finetuned

🚀 BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

This is a model card for image captioning. The model is pre - trained on the COCO dataset (base architecture with ViT base backbone) and fine - tuned on the football dataset. It can be used for both conditional and unconditional image captioning.

Google Colab notebook for fine - tuning: https://colab.research.google.com/drive/1lbqiSiA0sDF7JDWPeS0tccrM85LloVha?usp=sharing

Pull figure from BLIP official repo

🚀 Quick Start

This model can be used for conditional and un - conditional image captioning. You can follow the usage examples below to start using it.

✨ Features

• Flexible Transfer: BLIP can be flexibly transferred to both vision - language understanding and generation tasks.
• Effective Use of Noisy Data: It effectively utilizes noisy web data by bootstrapping the captions, with a captioner generating synthetic captions and a filter removing the noisy ones.
• State - of - the - Art Results: Achieves state - of - the - art results on a wide range of vision - language tasks, such as image - text retrieval, image captioning, and VQA.
• Strong Generalization: Demonstrates strong generalization ability when directly transferred to video - language tasks in a zero - shot manner.

💻 Usage Examples

Basic Usage

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

Using the Pytorch model

Running the model on CPU

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

processor = BlipProcessor.from_pretrained("ybelkada/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("ybelkada/blip-image-captioning-base")

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))
# >>> a photography of a woman and her dog

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

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog

Running the model on GPU

In full precision

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

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesfoce/blip-image-captioning-base").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))
# >>> a photography of a woman and her dog

# 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))
>>> a woman sitting on the beach with her dog

In half precision (float16)

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

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", 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 her dog

# 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))
>>> a woman sitting on the beach with her dog

📚 Documentation

TL;DR

The authors from the paper 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.

📄 License

This model is licensed under the bsd - 3 - clause 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}
}