Update README.md (#2)

- Update README.md (cc461ef2c88e82f7dc96831a4c922a00fc1e3a24)


Co-authored-by: Ziyue Wang <[email protected]>

README.md

@@ -2,10 +2,10 @@
 license: apache-2.0
 pipeline_tag: image-segmentation
 tags:
-- medica
+- medical
 ---
 
-Welcome Medical Adapters Zoo (Med-Adpt Zoo)!
+Welcome to Medical Adapters Zoo (Med-Adpt Zoo)!
 
 ## Med-Adpt Zoo Map 🐘🐊🦍🦒🦨🦜🦥
 
@@ -27,14 +27,42 @@ Check our paper: [Medical SAM Adapter](https://arxiv.org/abs/2304.12620) for the
 
 ## Why
 
-SAM (Segment Anything Model) is one of the most popular open model for the image segmentation. Unfortaintly, it does not perform well on the medical images. 
+SAM (Segment Anything Model) is one of the most popular open models for image segmentation. Unfortunately, it does not perform well on the medical images. 
 An efficient way to solve it is using Adapters, i.e., some layers with a few parameters to be added to the pre-trained SAM model to fine-tune it to the target down-stream tasks.
 Medical image segmentation includes many different organs, lesions, abnormalities as the targets.
-So we are training different adapter for each of the target, and share them here for the easy usage in the community.
+So we are training different adapters for each of the targets, and sharing them here for the easy usage in the community.
 
 Download an adapter for your target disease—trained on organs, lesions, and abnormalities—and effortlessly enhance SAM.
 
-One adapter tranfers your SAM into a medical domain expert. Give it a try! 
+One adapter transfers your SAM to a medical domain expert. Give it a try! 
+
+## How to Use
+
+1. Download the code of our MedSAM-Adapter [here](https://github.com/KidsWithTokens/Medical-SAM-Adapter).
+2. Download the weights of the original SAM model.
+3. Load the original model and our adapter for downstream tasks.
+
+```python
+import torch
+import torchvision.transforms as transforms
+
+import cfg
+from utils import *
+
+# set your own configs
+args = cfg.parse_args()
+GPUdevice = torch.device('cuda', args.gpu_device)
+
+# load the original SAM model 
+net = get_network(args, args.net, use_gpu=args.gpu, gpu_device=GPUdevice, distribution = args.distributed)
+
+# load task-specific adapter
+adapter_path = 'OpticCup_Fundus_SAM_1024.pth'
+adapter = torch.load(adapter_path)['state_dict']
+for name, param in adapter.items():
+    if name in adapter:
+        net.state_dict()[name].copy_(param)
+```
 
 ## Authorship