Add model card

README.md

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+---
+library_name: pytorch
+tags:
+- opensdi
+- maskclip
+- diffusion-detection
+- image-forensics
+- forgery-localization
+- pytorch
+datasets:
+- nebula/OpenSDI_train
+- nebula/OpenSDI_test
+---
+
+# MaskCLIP Weights for OpenSDI
+
+This repository hosts model checkpoints for **OpenSDI: Spotting Diffusion-Generated Images in the Open World**.
+
+## Links
+
+- Model weights: https://huggingface.co/nebula/MaskCLIP-weights/tree/main
+- Code: https://github.com/iamwangyabin/OpenSDI
+- Project page: https://iamwangyabin.github.io/OpenSDI/
+- Paper: https://arxiv.org/abs/2503.19653
+- Training dataset: https://huggingface.co/datasets/nebula/OpenSDI_train
+- Testing dataset: https://huggingface.co/datasets/nebula/OpenSDI_test
+
+## Checkpoints
+
+The `Files and versions` tab contains `.pth` checkpoints for MaskCLIP and related OpenSDI baselines. For MaskCLIP evaluation, download one of the `MaskCLIP_sd15_*.pth` checkpoints and use it with the OpenSDI codebase.
+
+Example:
+
+```bash
+hf download nebula/MaskCLIP-weights MaskCLIP_sd15_20241103_17_45_16.pth --local-dir weights
+```
+
+Then set `--checkpoint_path` in `test.sh` to the downloaded checkpoint path, for example:
+
+```bash
+--checkpoint_path "weights/MaskCLIP_sd15_20241103_17_45_16.pth"
+```
+
+## Citation
+
+If you find OpenSDI useful for your research and applications, please cite:
+
+```bibtex
+@InProceedings{wang2025opensdi,
+    author={Wang, Yabin and Huang, Zhiwu and Hong, Xiaopeng},
+    title={OpenSDI: Spotting Diffusion-Generated Images in the Open World},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year={2025}
+}
+```