YearCLIP

YearCLIP: Beyond Memorization

This is the official checkpoint for YearCLIP, introduced in the paper Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models.

Model Details

• Model Architecture: YearCLIP (CLIP-based with Ordinal Regression Head)
• Task: Building Age Estimation (Year Prediction)
• Dataset: YearGuessr
• Performance: MAE 39.26 years (on YearGuessr Test Split)

Usage

Please refer to our GitHub Repository for installation and inference instructions.

To download this checkpoint manually in python:

from huggingface_hub import hf_hub_download

checkpoint_path = hf_hub_download(repo_id="Morris0401/YearCLIP", filename="yearclip_best.pt")
print(f"Model downloaded to: {checkpoint_path}")

Citation

If you find this dataset helpful, please consider citing:

@misc{szutu2025memorizationmultimodalordinalregression,
      title={Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models},
      author={Li-Zhong Szu-Tu and Ting-Lin Wu and Chia-Jui Chang and He Syu and Yu-Lun Liu},
      year={2025},
      eprint={2512.21337},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={[https://arxiv.org/abs/2512.21337](https://arxiv.org/abs/2512.21337)},
}