diff --git a/IGEV-Stereo/core/extractor.py b/IGEV-Stereo/core/extractor.py
index 307df38..35e3f35 100644
--- a/IGEV-Stereo/core/extractor.py
+++ b/IGEV-Stereo/core/extractor.py
@@ -325,21 +325,24 @@ class SubModule(nn.Module):
 
 
 class Feature(SubModule):
-    def __init__(self):
+    def __init__(self, freeze):
         super(Feature, self).__init__()
         pretrained =  True
-        model = timm.create_model('mobilenetv2_100', pretrained=pretrained, features_only=True)
+        self.model = timm.create_model('mobilenetv2_100', pretrained=pretrained, features_only=True)
+        if freeze:
+            for p in self.model.parameters():
+                p.requires_grad = False
         layers = [1,2,3,5,6]
         chans = [16, 24, 32, 96, 160]
-        self.conv_stem = model.conv_stem
-        self.bn1 = model.bn1
-        self.act1 = model.act1
+        self.conv_stem = self.model.conv_stem
+        self.bn1 = self.model.bn1
+        self.act1 = self.model.act1
 
-        self.block0 = torch.nn.Sequential(*model.blocks[0:layers[0]])
-        self.block1 = torch.nn.Sequential(*model.blocks[layers[0]:layers[1]])
-        self.block2 = torch.nn.Sequential(*model.blocks[layers[1]:layers[2]])
-        self.block3 = torch.nn.Sequential(*model.blocks[layers[2]:layers[3]])
-        self.block4 = torch.nn.Sequential(*model.blocks[layers[3]:layers[4]])
+        self.block0 = torch.nn.Sequential(*self.model.blocks[0:layers[0]])
+        self.block1 = torch.nn.Sequential(*self.model.blocks[layers[0]:layers[1]])
+        self.block2 = torch.nn.Sequential(*self.model.blocks[layers[1]:layers[2]])
+        self.block3 = torch.nn.Sequential(*self.model.blocks[layers[2]:layers[3]])
+        self.block4 = torch.nn.Sequential(*self.model.blocks[layers[3]:layers[4]])
 
         self.deconv32_16 = Conv2x_IN(chans[4], chans[3], deconv=True, concat=True)
         self.deconv16_8 = Conv2x_IN(chans[3]*2, chans[2], deconv=True, concat=True)
diff --git a/IGEV-Stereo/core/igev_stereo.py b/IGEV-Stereo/core/igev_stereo.py
index c432092..acb316e 100644
--- a/IGEV-Stereo/core/igev_stereo.py
+++ b/IGEV-Stereo/core/igev_stereo.py
@@ -100,7 +100,7 @@ class IGEVStereo(nn.Module):
 
         self.context_zqr_convs = nn.ModuleList([nn.Conv2d(context_dims[i], args.hidden_dims[i]*3, 3, padding=3//2) for i in range(self.args.n_gru_layers)])
 
-        self.feature = Feature()
+        self.feature = Feature(args.freeze_backbone_params)
 
         self.stem_2 = nn.Sequential(
             BasicConv_IN(3, 32, kernel_size=3, stride=2, padding=1),
diff --git a/IGEV-Stereo/train_stereo.py b/IGEV-Stereo/train_stereo.py
index 7e3e493..19262c5 100644
--- a/IGEV-Stereo/train_stereo.py
+++ b/IGEV-Stereo/train_stereo.py
@@ -78,7 +78,7 @@ def sequence_loss(disp_preds, disp_init_pred, disp_gt, valid, loss_gamma=0.9, ma
 
 def fetch_optimizer(args, model):
     """ Create the optimizer and learning rate scheduler """
-    optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=1e-8)
+    optimizer = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wdecay, eps=1e-8)
 
     # todo: cosine scheduler, warm-up
     scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps+100,
@@ -222,6 +222,7 @@ if __name__ == '__main__':
     parser.add_argument('--train_datasets', nargs='+', default=['sceneflow'], help="training datasets.")
     parser.add_argument('--lr', type=float, default=0.0002, help="max learning rate.")
     parser.add_argument('--num_steps', type=int, default=200000, help="length of training schedule.")
+    parser.add_argument('--freeze_backbone_params', action="store_true", help="freeze backbone parameters")
     parser.add_argument('--image_size', type=int, nargs='+', default=[320, 736], help="size of the random image crops used during training.")
     parser.add_argument('--train_iters', type=int, default=22, help="number of updates to the disparity field in each forward pass.")
     parser.add_argument('--wdecay', type=float, default=.00001, help="Weight decay in optimizer.")