ShareLock: Ultra-Lightweight CLIP-like Vision-Language Model

Welcome to the Hugging Face repository for ShareLock, an ultra-lightweight CLIP-like vision-language model. This repository hosts pretrained checkpoints for ShareLock, enabling easy integration into your projects.

ShareLock is introduced in the paper:
"Do Better Language Models Have Crisper Vision?"
Jona Ruthardt, Gertjan J. Burghouts, Serge Belongie, Yuki M. Asano

🌐 Project Page ⌨️ GitHub Repository πŸ“„ Read the Paper on arXiv


🧠 Model Overview

ShareLock combines strong frozen features from unimodal vision and language models to achieve competitive multimodal performance with minimal resources.

Key Highlights:

  • Ultra-Lightweight: ShareLock is trained on only 563k image-caption pairs, requiring just 1 GPU hour.
  • Efficient Performance: Achieves 51% zero-shot accuracy on ImageNet.
  • Plug-and-Play: Easily integrates into downstream vision-language tasks.

πŸ“‚ Available Checkpoints

Model Variants:

  1. ShareLock trained on CC3M
  2. ShareLock trained on CC12M

πŸš€ Usage

You can load ShareLock models using the ShareLock class directly for inference or fine-tuning:

Example: Zero-shot Classification

from sharelock.models.model import ShareLock

# Path to the checkpoint
checkpoint_path = "path/to/checkpoint.ckpt"
config = {
    # Add your configuration for model hyperparameters etc. here
}

# Load the ShareLock model
model = ShareLock.load_from_checkpoint(checkpoint_path, config=config)

# Encode text and images for multimodal tasks
image_embeddings = model.encode_image(your_image_tensor)
text_embeddings = model.encode_text(["a cat", "a dog"])

# Perform multimodal operations

πŸ› οΈ Details

For training scripts, evaluation, or further implementation details, visit our GitHub repository


πŸ“œ Citation

If you use ShareLock in your research, please cite:

@article{ruthardt2024sharelock,
  title={Do Better Language Models Have Crisper Vision?},
  author={Jona Ruthardt and Gertjan J. Burghouts and Serge Belongie and Yuki M. Asano},
  journal={arXiv preprint arXiv:2410.07173},
  year={2024}
}

πŸ“§ Contact

For any questions or collaborations, feel free to reach out to Jona Ruthardt.

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