diff --git a/tests/test_backbone.py b/tests/test_backbone.py
index 171344ab4..3f88802f2 100644
--- a/tests/test_backbone.py
+++ b/tests/test_backbone.py
@@ -97,7 +97,7 @@ def test_mobilenetv2_backbone():
         for param in layer.parameters():
             assert param.requires_grad is False
 
-    # Test MobileNetv2 with first stage frozen
+    # Test MobileNetv2 with bn frozen
     model = MobileNetv2(bn_frozen=True)
     model.init_weights()
     model.train()
@@ -163,25 +163,6 @@ def test_mobilenetv2_backbone():
     assert feat[5].shape == torch.Size([1, 160, 7, 7])
     assert feat[6].shape == torch.Size([1, 320, 7, 7])
 
-    # Test MobileNetv2 with BatchNorm forward
-    model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU6)
-    for m in model.modules():
-        if is_norm(m):
-            assert isinstance(m, _BatchNorm)
-    model.init_weights()
-    model.train()
-
-    imgs = torch.randn(1, 3, 224, 224)
-    feat = model(imgs)
-    assert len(feat) == 8
-    assert feat[0].shape == torch.Size([1, 16, 112, 112])
-    assert feat[1].shape == torch.Size([1, 24, 56, 56])
-    assert feat[2].shape == torch.Size([1, 32, 28, 28])
-    assert feat[3].shape == torch.Size([1, 64, 14, 14])
-    assert feat[4].shape == torch.Size([1, 96, 14, 14])
-    assert feat[5].shape == torch.Size([1, 160, 7, 7])
-    assert feat[6].shape == torch.Size([1, 320, 7, 7])
-
     # Test MobileNetv2 with layers 1, 3, 5 out forward
     model = MobileNetv2(
         widen_factor=1.0, activation=nn.ReLU6, out_indices=(0, 2, 4))