Create Readme.md

README.md

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+---
+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](https://arxiv.org/abs/2512.21337).
+
+## Model Details
+- **Model Architecture**: YearCLIP (CLIP-based with Ordinal Regression Head)
+- **Task**: Building Age Estimation (Year Prediction)
+- **Dataset**: [YearGuessr](https://huggingface.co/datasets/Morris0401/Year-Guessr-Dataset)
+- **Performance**: MAE 39.26 years (on YearGuessr Test Split)
+
+## Usage
+
+Please refer to our [GitHub Repository](https://github.com/Sytwu/BeyondMemo) for installation and inference instructions.
+
+To download this checkpoint manually in python:
+
+```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:
+
+```bibtex
+@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)},
+}
+```