diff --git a/anomalib/models/padim/torch_model.py b/anomalib/models/padim/torch_model.py
index 72d8c73c66..ace09f9a7d 100644
--- a/anomalib/models/padim/torch_model.py
+++ b/anomalib/models/padim/torch_model.py
@@ -14,11 +14,6 @@
 from anomalib.models.padim.anomaly_map import AnomalyMapGenerator
 from anomalib.pre_processing import Tiler
 
-_DIMS = {
-    "resnet18": {"orig_dims": 448, "emb_scale": 4},
-    "wide_resnet50_2": {"orig_dims": 1792, "emb_scale": 4},
-}
-
 # defaults from the paper
 _N_FEATURES_DEFAULTS = {
     "resnet18": 100,
@@ -31,6 +26,9 @@ def _deduce_dims(
 ) -> Tuple[int, int]:
     """Run a dry run to deduce the dimensions of the extracted features.
 
+    Important: `layers` is assumed to be ordered and the first (layers[0])
+                is assumed to be the layer with largest resolution.
+
     Returns:
         Tuple[int, int]: Dimensions of the extracted features: (n_dims_original, n_patches)
     """
@@ -72,31 +70,22 @@ def __init__(
         self.backbone = backbone
         self.layers = layers
         self.feature_extractor = FeatureExtractor(backbone=self.backbone, layers=layers, pre_trained=pre_trained)
+        self.n_features_original, self.n_patches = _deduce_dims(self.feature_extractor, input_size, self.layers)
 
-        if backbone in _DIMS:
-            backbone_dims = _DIMS[backbone]
-            self.n_features_original = backbone_dims["orig_dims"]
-            emb_scale = backbone_dims["emb_scale"]
-            patches_dims = torch.tensor(input_size) / emb_scale
-            self.n_patches = patches_dims.ceil().prod().int().item()
-
-        else:
-            self.n_features_original, self.n_patches = _deduce_dims(self.feature_extractor, input_size, self.layers)
+        n_features = n_features or _N_FEATURES_DEFAULTS.get(self.backbone)
 
         if n_features is None:
+            raise ValueError(
+                f"n_features must be specified for backbone {self.backbone}. "
+                f"Default values are available for: {sorted(_N_FEATURES_DEFAULTS.keys())}"
+            )
 
-            if self.backbone in _N_FEATURES_DEFAULTS:
-                n_features = _N_FEATURES_DEFAULTS[self.backbone]
-
-            else:
-                raise ValueError(
-                    f"{self.__class__.__name__}.n_features must be specified for backbone {self.backbone}. "
-                    f"Default values are available for: {sorted(_N_FEATURES_DEFAULTS.keys())}"
-                )
         assert (
-            n_features <= self.n_features_original
-        ), f"n_features ({n_features}) must be <= n_features_original ({self.n_features_original})"
+            0 < n_features <= self.n_features_original
+        ), f"for backbone {self.backbone}, 0 < n_features <= {self.n_features_original}, found {n_features}"
+
         self.n_features = n_features
+
         # pylint: disable=not-callable
         # Since idx is randomly selected, save it with model to get same results
         self.register_buffer(