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--- |
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license: mit |
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tags: |
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- vision |
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- image-regression |
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- building-age |
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- clip |
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- ordinal-regression |
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library_name: pytorch |
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pipeline_tag: image-feature-extraction |
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--- |
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# YearCLIP: Beyond Memorization |
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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](https://arxiv.org/abs/2512.21337). |
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## Model Details |
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- **Model Architecture**: YearCLIP (CLIP-based with Ordinal Regression Head) |
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- **Task**: Building Age Estimation (Year Prediction) |
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- **Dataset**: [YearGuessr](https://huggingface.co/datasets/Morris0401/Year-Guessr-Dataset) |
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- **Performance**: MAE 39.26 years (on YearGuessr Test Split) |
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## Usage |
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Please refer to our [GitHub Repository](https://github.com/Sytwu/BeyondMemo) for installation and inference instructions. |
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To download this checkpoint manually in python: |
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```python |
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from huggingface_hub import hf_hub_download |
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checkpoint_path = hf_hub_download(repo_id="Morris0401/YearCLIP", filename="yearclip_best.pt") |
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print(f"Model downloaded to: {checkpoint_path}") |
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``` |
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## Citation |
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If you find this dataset helpful, please consider citing: |
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```bibtex |
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@misc{szutu2025memorizationmultimodalordinalregression, |
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title={Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models}, |
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author={Li-Zhong Szu-Tu and Ting-Lin Wu and Chia-Jui Chang and He Syu and Yu-Lun Liu}, |
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year={2025}, |
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eprint={2512.21337}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={[https://arxiv.org/abs/2512.21337](https://arxiv.org/abs/2512.21337)}, |
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} |
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``` |
