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-# Long-CLIP
-This repository is the official implementation of Long-CLIP
-
-**Long-CLIP: Unlocking the Long-Text Capability of CLIP**\
-[Beichen Zhang](https://beichenzbc.github.io), [Pan Zhang](https://panzhang0212.github.io/), [Xiaoyi Dong](https://lightdxy.github.io/), [Yuhang Zang](https://yuhangzang.github.io/), [Jiaqi Wang](https://myownskyw7.github.io/)
-
-## 💡 Highlights
-- 🔥 **Long Input length** Increase the maximum input length of CLIP from **77** to **248**.
-- 🔥 **Strong Performace** Improve the R@5 of long-caption text-image retrieval by **20%** and traditional text-image retrieval by **6%**.
-- 🔥 **Plug-in and play** Can be directly applied in **any work** that requires long-text capability.
-
-
-## 📜 News
-🚀 [2024/7/3] Our paper has been accepted by ***ECCV2024***.
-
-🚀 [2024/7/3] We release the code of using Long-CLIP in ***SDXL***. For detailed information, you may refer to `SDXL/SDXL.md`.
-
-🚀 [2024/5/21] We update the paper and checkpoints after fixing the bug in DDP and add results in Urban-1k. Special thanks to @MajorDavidZhang for finding and refining this bug in DDP! Now the fine-tuning only takes ***0.5*** hours on *8 GPUs*!
-
-🚀 [2024/5/21] Urban-1k: a scaling-up version of Urban-200 dataset in the paper has been released at this [page](https://huggingface.co/datasets/BeichenZhang/Urban1k).
-
-🚀 [2024/4/1] The training code is released!
-
-🚀 [2024/3/25] The Inference code and models ([LongCLIP-B](https://huggingface.co/BeichenZhang/LongCLIP-B) and [LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L)) are released!
-
-🚀 [2024/3/25] The [paper](https://arxiv.org/abs/2403.15378) is released!
-
-## 👨‍💻 Todo
-- [x] Training code for Long-CLIP based on OpenAI-CLIP
-- [x] Evaluation code for Long-CLIP
-- [x] evaluation code for zero-shot classification and text-image retrieval tasks.
-- [x] Usage example of Long-CLIP
-- [x] Checkpoints of Long-CLIP
-
-
-## 🛠️ Usage
-
-### Installation
-
-Our model is based on [CLIP](https://github.com/openai/CLIP), please prepare environment for CLIP.
-
-
-### how to use
-
-Please first clone our [repo](https://github.com/beichenzbc/Long-CLIP) from github by running the following command.
-
-```shell
-git clone https://github.com/beichenzbc/Long-CLIP.git
-cd Long-CLIP
-```
-
-Then, download the checkpoints of our model [LongCLIP-B](https://huggingface.co/BeichenZhang/LongCLIP-B) and/or [LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L) and place it under `./checkpoints`
-
-```python
-from model import longclip
-import torch
-from PIL import Image
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model, preprocess = longclip.load("./checkpoints/longclip-B.pt", device=device)
-
-text = longclip.tokenize(["A man is crossing the street with a red car parked nearby.", "A man is driving a car in an urban scene."]).to(device)
-image = preprocess(Image.open("./img/demo.png")).unsqueeze(0).to(device)
-
-with torch.no_grad():
-    image_features = model.encode_image(image)
-    text_features = model.encode_text(text)
-    
-    logits_per_image = image_features @ text_features.T
-    probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-
-print("Label probs:", probs) 
-```
-
-### Evaluation
-#### Zero-shot classification
-
-To run zero-shot classification on imagenet dataset, run the following command after preparing the data
-```shell
-cd eval/classification/imagenet
-python imagenet.py
-```
-
-Similarly, run the following command for cifar datset
-```shell
-cd eval/classification/cifar
-python cifar10.py               #cifar10
-python cifar100.py              #cifar100
-```
-
-#### Retrieval
-To run text-image retrieval on COCO2017 or Flickr30k, run the following command after preparing the data
-```shell
-cd eval/retrieval
-python coco.py                  #COCO2017
-python flickr30k.py             #Flickr30k
-```
-### Traning
-Please refer to `train/train.md` for training details.
-
-## ⭐ Demos
-### Long-CLIP-SDXL
-<p align="center"> <a>  
-<img src="./img/demo_SDXL.png"  width="900" />
-</a> </p>
-
-### Long-caption text-image retrieval 
-<p align="center"> <a>  
-<img src="./img/retrieval.png"  width="900" />
-</a> </p>
-
-### Plug-and-Play text to image generation 
-<p align="center"> <a>  
-<img src="./img/generation.png"  width="900" />
-</a> </p>
-
-
-## Citation
-If you find our work helpful for your research, please consider giving a citation:
-```
-@article{zhang2024longclip,
-        title={Long-CLIP: Unlocking the Long-Text Capability of CLIP},
-        author={Beichen Zhang and Pan Zhang and Xiaoyi Dong and Yuhang Zang and Jiaqi Wang},
-        journal={arXiv preprint arXiv:2403.15378},
-        year={2024}
-}
-```