Long-CLIP

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

💡 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

🚀 [2024/4/1] The training code is released!

🚀 [2024/3/25] The Inference code and models (LongCLIP-B and LongCLIP-L) are released!

🚀 [2024/3/25] The paper is released!

👨‍💻 Todo

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

🛠️ Usage

Installation

Our model is based on CLIP, please prepare environment for CLIP.

how to use

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, logits_per_text = model(image, text)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("Label probs:", probs) # prints: [[0.982  0.01799]]

Evaluation

Zero-shot classification

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

Retrieval

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

Traning

Please refer to train/train.md for training details.

bryant1410/Long-CLIP