Add pipeline tag and library name to model card

This PR adds the `pipeline_tag: image-classification` and `library_name: transformers` to the model card metadata. The pipeline tag makes the model discoverable through the Hugging Face pipeline interface for image classification tasks. The library name clarifies the underlying framework used for the model.

README.md

@@ -1,18 +1,18 @@
 ---
-license: mit
-
 datasets:
 - ILSVRC/imagenet-1k
 - phiyodr/coco2017
 - 1aurent/ADE20K
 language:
 - en
+license: mit
 metrics:
 - accuracy
 - mean_iou
+pipeline_tag: image-classification
+library_name: transformers
 ---
 
-
 # Quantized Spike-driven Transformer ([ICLR25](https://arxiv.org/abs/2501.13492))
 
 [Xuerui Qiu](https://scholar.google.com/citations?user=bMwW4e8AAAAJ&hl=zh-CN), [Malu Zhang](), [Jieyuan Zhang](https://www.ericzhuestc.site/), [Wenjie Wei](), [Honglin Cao](), [Junsheng Guo](), [Rui-Jie Zhu](https://scholar.google.com/citations?user=08ITzJsAAAAJ&hl=zh-CN),[Yimeng Shan](),[Yang Yang](),  [Haizhou Li](https://www.colips.org/~eleliha/)
@@ -25,6 +25,7 @@ Institute of Automation, Chinese Academy of Sciences
 
 :rocket:  :rocket:  :rocket: **News**:
 
+- **Feb. 24, 2025**: Checkpoint is available at [Huggingface](https://huggingface.co/Xuerui123/QSD_Transformer).
 - **Jan. 24, 2025**: Release the code for training and testing.
 
 ## Abstract
@@ -50,9 +51,11 @@ If you find this repository useful, please consider giving a star ⭐ and citati
 }
 ```
 
+Code: https://github.com/bollossom/QSD-Transformer
+
 For help or issues using this git, please submit a GitHub issue.
 
 For other communications related to this git, please contact  `qiuxuerui2024@ia.ac.cn`.
 
 ## Acknowledgement
-The object detection and semantic segmentation parts are based on [MMDetection](https://github.com/open-mmlab/mmdetection) and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) respectively. Thanks for their wonderful work.
+The object detection and semantic segmentation parts are based on [MMDetection](https://github.com/open-mmlab/mmdetection) and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) respectively. Thanks for their wonderful work.