Add pipeline tag, license and model checkpoints

This PR adds the `pipeline_tag` as `image-classification` and the `library_name` as `pytorch` to the metadata to ensure the model shows up in the image classification category.
It also adds the `license` based on the information in the GitHub README.
The model checkpoints are also added to the model card to make them more easily accessible.

README.md

@@ -1,9 +1,12 @@
 ---
+datasets:
+- imagenet-1k
 tags:
 - mae
 - crossmae
-datasets:
-- imagenet-1k
+pipeline_tag: image-classification
+library_name: pytorch
+license: cc-by-nc-4.0
 ---
 
 ## CrossMAE: Rethinking Patch Dependence for Masked Autoencoders
@@ -19,3 +22,60 @@ by <a href="https://max-fu.github.io">Letian Fu*</a>, <a href="https://tonylian.
 This repo has the models for [CrossMAE: Rethinking Patch Dependence for Masked Autoencoders](https://arxiv.org/abs/2401.14391).
 
 Please take a look at the [GitHub repo](https://github.com/TonyLianLong/CrossMAE) to see instructions on pretraining, fine-tuning, and evaluation with these models.
+
+<table><tbody>
+<!-- START TABLE -->
+<!-- TABLE HEADER -->
+<th valign="bottom"></th>
+<th valign="bottom">ViT-Small</th>
+<th valign="bottom">ViT-Base</th>
+<th valign="bottom">ViT-Base<sub>448</sub></th>
+<th valign="bottom">ViT-Large</th>
+<th valign="bottom">ViT-Huge</th>
+<!-- TABLE BODY -->
+<tr><td align="left">pretrained checkpoint</td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vits-mr0.75-kmr0.75-dd12/imagenet-mae-cross-vits-pretrain-wfm-mr0.75-kmr0.75-dd12-ep800-ui.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vitb-mr0.75-kmr0.75-dd12/imagenet-mae-cross-vitb-pretrain-wfm-mr0.75-kmr0.75-dd12-ep800-ui.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vitb-mr0.75-kmr0.75-dd12-448-400/imagenet-mae-cross-vitb-pretrain-wfm-mr0.75-kmr0.25-dd12-ep400-ui-res-448.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vitl-mr0.75-kmr0.75-dd12/imagenet-mae-cross-vitl-pretrain-wfm-mr0.75-kmr0.75-dd12-ep800-ui.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vith-mr0.75-kmr0.25-dd12/imagenet-mae-cross-vith-pretrain-wfm-mr0.75-kmr0.25-dd12-ep800-ui.pth?download=true'>download</a></td>
+</tr>
+<tr><td align="left">fine-tuned checkpoint</td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vits-mr0.75-kmr0.75-dd12/imagenet-mae-cross-vits-finetune-wfm-mr0.75-kmr0.75-dd12-ep800-ui.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vitb-mr0.75-kmr0.75-dd12/imagenet-mae-cross-vitb-finetune-wfm-mr0.75-kmr0.75-dd12-ep800-ui.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vitb-mr0.75-kmr0.75-dd12-448-400/imagenet-mae-cross-vitb-finetune-wfm-mr0.75-kmr0.25-dd12-ep400-ui-res-448.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vitl-mr0.75-kmr0.75-dd12/imagenet-mae-cross-vitl-finetune-wfm-mr0.75-kmr0.75-dd12-ep800-ui.pth?download=true'>download</a></td>
+<td align="center"><a href='https://huggingface.co/longlian/CrossMAE/resolve/main/vith-mr0.75-kmr0.25-dd12/imagenet-mae-cross-vith-finetune-wfm-mr0.75-kmr0.25-dd12-ep800-ui.pth?download=true'>download</a></td>
+</tr>
+<tr><td align="left"><b>Reference ImageNet accuracy (ours)</b></td>
+<td align="center"><b>79.318</b></td>
+<td align="center"><b>83.722</b></td>
+<td align="center"><b>84.598</b></td>
+<td align="center"><b>85.432</b></td>
+<td align="center"><b>86.256</b></td>
+</tr>
+<tr><td align="left">MAE ImageNet accuracy (baseline)</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center">84.8</td>
+<td align="center"></td>
+<td align="center">85.9</td>
+</tr>
+</tbody></table>
+
+## Citation
+Please give us a star 🌟 on Github to support us!
+
+Please cite our work if you find our work inspiring or use our code in your work:
+```
+@article{
+    fu2025rethinking,
+    title={Rethinking Patch Dependence for Masked Autoencoders},
+    author={Letian Fu and Long Lian and Renhao Wang and Baifeng Shi and XuDong Wang and Adam Yala and Trevor Darrell and Alexei A Efros and Ken Goldberg},
+    journal={Transactions on Machine Learning Research},
+    issn={2835-8856},
+    year={2025},
+    url={https://openreview.net/forum?id=JT2KMuo2BV},
+    note={}
+}
+```