This repository is the official implementation of Long-CLIP
Long-CLIP: Unlocking the Long-Text Capability of CLIP
Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, Jiaqi Wang
- π₯ Long Input length Increase the maximum input length of CLIP from 77 to 248.
- π₯ Strong Performace Improve the R@5 of long-caption text-image retrieval by 20% and traditional text-image retrieval by 6%.
- π₯ Plug-in and play Can be directly applied in any work that requires long-text capability.
π [2024/7/3] Our paper has been accepted by ECCV2024.
π [2024/7/3] We release the code of using Long-CLIP in SDXL. For detailed information, you may refer to SDXL/SDXL.md
.
π [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!
π [2024/5/21] Urban-1k: a scaling-up version of Urban-200 dataset in the paper has been released at this page.
π [2024/4/1] The training code is released!
π [2024/3/25] The Inference code and models (LongCLIP-B and LongCLIP-L) are released!
π [2024/3/25] The paper is released!
- Training code for Long-CLIP based on OpenAI-CLIP
- Evaluation code for Long-CLIP
- evaluation code for zero-shot classification and text-image retrieval tasks.
- Usage example of Long-CLIP
- Checkpoints of Long-CLIP
Our model is based on CLIP, please prepare environment for CLIP.
Please first clone our repo from github by running the following command.
git clone https://github.com/beichenzbc/Long-CLIP.git
cd Long-CLIP
Then, download the checkpoints of our model LongCLIP-B and/or LongCLIP-L and place it under ./checkpoints
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)
To run zero-shot classification on imagenet dataset, run the following command after preparing the data
cd eval/classification/imagenet
python imagenet.py
Similarly, run the following command for cifar datset
cd eval/classification/cifar
python cifar10.py #cifar10
python cifar100.py #cifar100
To run text-image retrieval on COCO2017 or Flickr30k, run the following command after preparing the data
cd eval/retrieval
python coco.py #COCO2017
python flickr30k.py #Flickr30k
Please refer to train/train.md
for training details.
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}
}