【ECCV 2024 🔥】 FreestyleRet: Retrieving Images from Style-Diversified Queries

If you like our project, please give us a star ⭐ on GitHub for latest update.

📰 News

• [2025.7.1] The Uni-Retrieval's expanded version Uni-RAG has been released at link!
• [2025.5.15] Our new retrieval model Uni-Retrieval has been accepted by ACL 2025!
• [2024.7.24] Our Diverse-Style Retrieval Dataset is released at this.
• [2024.7.24] Add a new retreival task evaluator tool with R1 and R5.
• [2024.7.1] Our FreestyleRet has been accepted by ECCV 2024!
• [2023.11.29] Code is available now! Welcome to watch 👀 this repository for the latest updates.

😮 Highlights

💡 High performance, plug-and-play, and lightweight

FreestyleRet is the first multi-style retrieval model and focus on the precision search field. You can transfer our gram-based style block to any other pre-trained model with only 28M trainable parameters.

⚡️ A multi-style, fully aligned and gained dataset

We propose the precision search task and its first corresponding dataset. Following figure shows our proposed Diverse-Style Retrieval Dataset(DSR), which includes five styles: origin, sketch, art, mosaic, and text.

🚀 Main Results

FreestyleRet achieves state-of-the-art (SOTA) performance on the DSR dataset and the ImageNet-X dataset, * donates the results of prompt tuning.

🤗 Visualization

Each sample has three images to compare the retrieval performance between our FreestyleRet and the BLIP baseline on the DSR dataset. The left images are the queries randomly selected from different styles. The middle and the right images are the retrieval results of our FreestyleRet-BLIP model and the original BLIP model, respectively.

🛠️ Requirements and Installation

• Python >= 3.9
• Pytorch >= 1.9.0
• CUDA Version >= 11.3
• Install required packages:

git clone https://github.com/YanhaoJia/FreeStyleRet
cd FreeStyleRet
pip install -r requirements.txt

💥 DSR dataset & FreestyleRet Checkpoints

Both dataset and model checkpoints has been released.

🗝️ Training & Validating

The training & validating instruction is in train.py and test.py.

👍 Acknowledgement

• OpenCLIP An open source pretraining framework.
• LanguageBind Bind five modalities through Language.
• ImageBind Bind five modalities through Image.
• FSCOCO An open source Sketch-Text retrieval dataset.

🔒 License

• The majority of this project is released under the MIT license as found in the LICENSE file.
• The dataset of this project is released under the CC-BY-NC 4.0 license as found in the DATASET_LICENSE file.

✏️ Citation

If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝.

@inproceedings{li2024freestyleret,
  title={Freestyleret: retrieving images from style-diversified queries},
  author={Li, Hao and Jia, Yanhao and Jin, Peng and Cheng, Zesen and Li, Kehan and Sui, Jialu and Liu, Chang and Yuan, Li},
  booktitle={European Conference on Computer Vision},
  pages={258--274},
  year={2024},
  organization={Springer}
}

@inproceedings{jia-etal-2025-uni,
    title = "Uni-Retrieval: A Multi-Style Retrieval Framework for {STEM}{'}s Education",
    author = "Yanhao, Jia and Xinyi, Wu and Hao, Li and Qinglin, Zhang and Yuxiao, Hu and Shuai, Zhao and Wenqi, Fan",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    year = "2025",
    publisher = "Association for Computational Linguistics",
    pages = "10182--10197",
    ISBN = "979-8-89176-251-0",
}

@misc{wu2025queryexplanationuniragmultimodal,
      title={From Query to Explanation: Uni-RAG for Multi-Modal Retrieval-Augmented Learning in STEM}, 
      author={Xinyi Wu and Yanhao Jia and Luwei Xiao and Shuai Zhao and Fengkuang Chiang and Erik Cambria},
      year={2025},
      eprint={2507.03868},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
}