metadata
license: mit
tags:
- vision
- image-regression
- building-age
- clip
- ordinal-regression
library_name: pytorch
pipeline_tag: image-feature-extraction
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)},
}