Update README.md

README.md

@@ -5,59 +5,3 @@ pipeline_tag: image-to-text
 tags:
 - image-captioning
 ---
-# FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions
-
-A framework designed to generate semantically rich image captions.
-
-## Resources
-
-- 💻 **Project Page**: For more details, visit the official [project page](https://rotsteinnoam.github.io/FuseCap/).
-
-- 📝 **Read the Paper**: You can find the paper [here](https://arxiv.org/abs/2305.17718).
-    
-- 🚀 **Demo**: Try out our BLIP-based model [demo](https://huggingface.co/spaces/noamrot/FuseCap) trained using FuseCap.
-
-- 📂 **Code Repository**: The code for FuseCap can be found in the [GitHub repository](https://github.com/RotsteinNoam/FuseCap).
-  
-- 🗃️ **Datasets**: The  fused captions datasets can be accessed from [here](https://github.com/RotsteinNoam/FuseCap#datasets).
-  
-#### Running the model
-
-Our BLIP-based model can be run using the following code,
-
-```python
-import requests
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-import torch
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-processor = BlipProcessor.from_pretrained("noamrot/FuseCap")
-model = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap").to(device)
-
-img_url = 'https://huggingface.co/spaces/noamrot/FuseCap/resolve/main/bike.jpg' 
-raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
-
-text = "a picture of "
-inputs = processor(raw_image, text, return_tensors="pt").to(device)
-
-out = model.generate(**inputs, num_beams = 3)
-print(processor.decode(out[0], skip_special_tokens=True))
-```
-
-## Upcoming Updates
-
-The official codebase, datasets and trained models for this project will be released soon.
-
-## BibTeX
-
-``` Citation
-@article{rotstein2023fusecap,
-      title={FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions}, 
-      author={Noam Rotstein and David Bensaid and Shaked Brody and Roy Ganz and Ron Kimmel},
-      year={2023},
-      eprint={2305.17718},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```