Update readme

Signed-off-by: heyufan1995 <heyufan1995@gmail.com>

Author: heyufan1995

vista3d/cvpr_workshop/README.md

@@ -15,7 +15,6 @@ limitations under the License.
 This repository is written for the "CVPR 2025: Foundation Models for Interactive 3D Biomedical Image Segmentation"([link](https://www.codabench.org/competitions/5263/)) challenge. It 
 is based on MONAI 1.4. Many of the functions in the main VISTA3D repository are moved to MONAI 1.4 and this simplified folder will directly use components from MONAI.
 
-
 It is simplified to train interactive segmentation models across different modalities. The sophisticated transforms and recipes used for VISTA3D are removed. The finetuned VISTA3D checkpoint on the challenge subsets is available [here](https://drive.google.com/file/d/1r2KvHP_30nHR3LU7NJEdscVnlZ2hTtcd/view?usp=sharing)
 
 # Setup
@@ -38,4 +37,3 @@ docker save -o vista3d.tar.gz vista3d:latest
 ```
 
 
-

vista3d/cvpr_workshop/train_cvpr.py

@@ -104,12 +104,15 @@ def __getitem__(self, idx):
         return data
 # Training function
 def train():
+    json_file = "subset.json"  # Update with your JSON file
     json_file = "subset.json"  # Update with your JSON file
     epoch_number = 100
     start_epoch = 0
     lr = 2e-5
     checkpoint_dir = "checkpoints"
     start_checkpoint = '/workspace/CPRR25_vista3D_model_final_10percent_data.pth'
+    start_checkpoint = '/workspace/CPRR25_vista3D_model_final_10percent_data.pth'
+
     os.makedirs(checkpoint_dir, exist_ok=True)
     dist.init_process_group(backend="nccl")
     world_size = int(os.environ["WORLD_SIZE"])
@@ -122,6 +125,8 @@ def train():
     model = vista3d132(in_channels=1).to(device)
     pretrained_ckpt = torch.load(start_checkpoint, map_location=device)
     # pretrained_ckpt = torch.load(os.path.join(checkpoint_dir, f"model_epoch{start_epoch}.pth"))
+    pretrained_ckpt = torch.load(start_checkpoint, map_location=device)
+    # pretrained_ckpt = torch.load(os.path.join(checkpoint_dir, f"model_epoch{start_epoch}.pth"))
     model = DDP(model, device_ids=[local_rank], find_unused_parameters=True)
     model.load_state_dict(pretrained_ckpt['model'], strict=True)
     optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1.0e-05)