Update README.md

README.md

@@ -1,3 +1,95 @@
----
-license: mit
----
+---
+license: apache-2.0
+---
+
+
+
+
+
+<br>
+
+
+
+# GroupMamba-Tiny Model Card
+
+
+
+## Model Details
+
+
+
+GroupMamba-Tiny is a generic backbone with 23M parameters trained on the ImageNet-1K dataset for vision tasks.
+
+
+
+- **Model type:** Parameter-Efficient and Accurate Vision Backbone Based on Group Visual State Space Model
+- **License:** Non-commercial license
+
+
+
+
+
+### Model Sources
+
+
+
+- **Repository:** https://github.com/amshaker/GroupMamba
+- **Paper:** https://arxiv.org/abs/X.X
+
+
+
+## Uses
+
+
+
+The primary use of GroupMamba is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.
+The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.
+
+
+
+## How to Get Started with the Model
+
+
+
+- You can replace the backbone for vision tasks with the proposed GroupMamba: https://github.com/Amshaker/GroupMamba/blob/main/classification/models/groupmamba.py
+- Then, you can load this checkpoint and start fine-tuning.
+
+
+
+## Training Details
+
+
+
+GroupMamba is pretrained on ImageNet-1K with classification supervision.
+The training data is around 1.3M images from [ImageNet-1K dataset](https://www.image-net.org/challenges/LSVRC/2012/).
+See more details in this [paper](https://arxiv.org/abs/X.X.
+
+
+
+## Evaluation
+
+
+
+GroupMamba-Tiny is evaluated on ImageNet-1K val set, and achieves 83.3% Top-1 Acc with only 23M parameters. See more details in this [paper](https://arxiv.org/abs/X.X).
+
+
+
+## Additional Information
+
+
+
+### Citation Information
+
+
+
+```
+@article{GroupMamba,
+  title={GroupMamba: Parameter-Efficient and Accurate Group Visual State Space Model},
+  author={Abdelrahman Shaker and Syed Talal Wasim and Salman Khan and Gall Jürgen and Fahad Khan},
+  journal={arXiv preprint arXiv:X.X},
+  year={2024}
+}
+```
+
+
+