diff --git a/models/vgg_.py b/models/vgg_.py
index 130083d..fbcef24 100644
--- a/models/vgg_.py
+++ b/models/vgg_.py
@@ -2,8 +2,12 @@
 """
 Mostly copy-paste from torchvision references.
 """
+from posixpath import split
 import torch
 import torch.nn as nn
+import os
+from tqdm import tqdm
+import urllib.request
 
 
 __all__ = [
@@ -23,14 +27,13 @@
     'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
 }
 
+checkpoints_dir = os.path.join(os.path.dirname(__file__), "../checkpoints/")
 
 model_paths = {
-    'vgg16_bn': '/apdcephfs/private_changanwang/checkpoints/vgg16_bn-6c64b313.pth',
-    'vgg16': '/apdcephfs/private_changanwang/checkpoints/vgg16-397923af.pth',
-
+    'vgg16_bn': os.path.join(checkpoints_dir,'vgg16_bn-6c64b313.pth'),
+    'vgg16': os.path.join(checkpoints_dir,'vgg16-397923af.pth'),
 }
 
-
 class VGG(nn.Module):
 
     def __init__(self, features, num_classes=1000, init_weights=True):
@@ -98,11 +101,30 @@ def make_layers(cfg, batch_norm=False, sync=False):
 }
 
 
+class DownloadProgressBar(tqdm):
+    def update_to(self, b=1, bsize=1, tsize=None):
+        if tsize is not None:
+            self.total = tsize
+        self.update(b * bsize - self.n)
+
+def download_arch_if_not_exists(arch):
+    if not os.path.exists(model_paths[arch]):
+        print(f"{arch} model not found.")
+        chunk_size = 1024
+        os.makedirs(checkpoints_dir, exist_ok=True)
+
+        url = model_urls[arch]
+        filename = os.path.basename(url)
+        output_path = os.path.join(checkpoints_dir, filename)
+        with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1]) as t:
+            urllib.request.urlretrieve(url, filename=output_path, reporthook=t.update_to)
+
 def _vgg(arch, cfg, batch_norm, pretrained, progress, sync=False, **kwargs):
     if pretrained:
         kwargs['init_weights'] = False
     model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, sync=sync), **kwargs)
     if pretrained:
+        download_arch_if_not_exists(arch)
         state_dict = torch.load(model_paths[arch])
         model.load_state_dict(state_dict)
     return model