diff --git a/tests/test_methods_utils.py b/tests/test_methods_utils.py
index 4792bf9..402477f 100644
--- a/tests/test_methods_utils.py
+++ b/tests/test_methods_utils.py
@@ -1,4 +1,4 @@
-from torchvision.models import resnet18
+from torchvision.models import resnet18, mobilenet_v3_large
 
 from torchcam.methods import _utils
 
@@ -8,6 +8,10 @@ def test_locate_candidate_layer(mock_img_model):
     mod = resnet18().eval()
     assert _utils.locate_candidate_layer(mod) == "layer4"
 
+    # Mobilenet V3 Large
+    mod = mobilenet_v3_large().eval()
+    assert _utils.locate_candidate_layer(mod) == "features"
+
     # Custom model
     mod = mock_img_model.train()
 
diff --git a/torchcam/methods/_utils.py b/torchcam/methods/_utils.py
index 6917ffb..a5697cf 100644
--- a/torchcam/methods/_utils.py
+++ b/torchcam/methods/_utils.py
@@ -51,12 +51,11 @@ def _record_output_shape(module: nn.Module, input: Tensor, output: Tensor, name:
 
     # Check output shapes
     candidate_layer = None
-    for layer_name, output_shape in output_shapes:
+    for layer_name, output_shape in output_shapes[::-1]:
         # Stop before flattening or global pooling
-        if len(output_shape) != (len(input_shape) + 1) or all(v == 1 for v in output_shape[2:]):
-            break
-        else:
+        if len(output_shape) == (len(input_shape) + 1) and any(v != 1 for v in output_shape[2:]):
             candidate_layer = layer_name
+            break
 
     return candidate_layer