Add project page

README.md

@@ -1,18 +1,18 @@
 ---
+datasets:
+- medical
 language: en
-license: cc-by-sa-4.0
 library_name: torch
-tags:
-  - medical
-  - segmentation
-  - sam
-  - medical-imaging
-  - ct
-  - mri
-  - ultrasound
+license: cc-by-sa-4.0
 pipeline_tag: image-segmentation
-datasets:
-  - medical
+tags:
+- medical
+- segmentation
+- sam
+- medical-imaging
+- ct
+- mri
+- ultrasound
 ---
 
 # MedSAM2: Segment Anything in 3D Medical Images and Videos
@@ -37,6 +37,7 @@ datasets:
  </table>
 </div>
 
+[Project Page](https://medsam2.github.io/)
 
 ## Authors
 
@@ -78,75 +79,4 @@ datasets:
 ## Model Overview
 MedSAM2 is a promptable segmentation segmentation model tailored for medical imaging applications. Built upon the foundation of the [Segment Anything Model (SAM) 2.1](https://github.com/facebookresearch/sam2), MedSAM2 has been specifically adapted and fine-tuned for various 3D medical images and videos.
 
-## Available Models
-
-- **MedSAM2_2411.pt**: Base model trained in November 2024
-- **MedSAM2_US_Heart.pt**: Fine-tuned model specialized for heart ultrasound video segmentation
-- **MedSAM2_MRI_LiverLesion.pt**: Fine-tuned model for liver lesion segmentation in MRI scans
-- **MedSAM2_CTLesion.pt**: Fine-tuned model for general lesion segmentation in CT scans
-- **MedSAM2_latest.pt** (recommended): Latest version trained on the combination of public datasets and newly annotated medical imaging data
-
-## Downloading Models
-
-### Option 1: Download individual models
-You can download the models directly from the Hugging Face repository:
-
-```python
-# Using huggingface_hub
-from huggingface_hub import hf_hub_download
-
-# Download the recommended latest model
-model_path = hf_hub_download(repo_id="wanglab/MedSAM2", filename="MedSAM2_latest.pt")
-
-# Or download a specific fine-tuned model
-heart_us_model_path = hf_hub_download(repo_id="wanglab/MedSAM2", filename="MedSAM2_US_Heart.pt")
-liver_model_path = hf_hub_download(repo_id="wanglab/MedSAM2", filename="MedSAM2_MRI_LiverLesion.pt")
-```
-
-### Option 2: Download all models to a specific folder
-```python
-from huggingface_hub import hf_hub_download
-import os
-
-# Create checkpoints directory if it doesn't exist
-os.makedirs("checkpoints", exist_ok=True)
-
-# List of model filenames
-model_files = [
-    "MedSAM2_2411.pt",
-    "MedSAM2_US_Heart.pt",
-    "MedSAM2_MRI_LiverLesion.pt",
-    "MedSAM2_CTLesion.pt",
-    "MedSAM2_latest.pt"
-]
-
-# Download all models
-for model_file in model_files:
-    local_path = os.path.join("checkpoints", model_file)
-    hf_hub_download(
-        repo_id="wanglab/MedSAM2",
-        filename=model_file,
-        local_dir="checkpoints",
-        local_dir_use_symlinks=False
-    )
-    print(f"Downloaded {model_file} to {local_path}")
-```
-
-Alternatively, you can manually download the models from the [Hugging Face repository page](https://huggingface.co/wanglab/MedSAM2).
-
-
-
-## Citations
-
-```
-@article{MedSAM2,
-    title={MedSAM2: Segment Anything in 3D Medical Images and Videos},
-    author={Ma, Jun and Yang, Zongxin and Kim, Sumin and Chen, Bihui and Baharoon, Mohammed and Fallahpour, Adibvafa and Asakereh, Reza and Lyu, Hongwei and Wang, Bo},
-    journal={arXiv preprint arXiv:2504.03600},
-    year={2025}
-}
-```
-
-## License
-
-The model weights can only be used for research and education purposes.
+<!-- rest of the model card -->