diff --git a/src/lightning/pytorch/utilities/model_summary/model_summary.py b/src/lightning/pytorch/utilities/model_summary/model_summary.py
index 01b692abdc05f..4760b76af9cb8 100644
--- a/src/lightning/pytorch/utilities/model_summary/model_summary.py
+++ b/src/lightning/pytorch/utilities/model_summary/model_summary.py
@@ -17,6 +17,7 @@
 import logging
 import math
 from collections import OrderedDict
+from enum import Enum
 from typing import Any, Optional, Union
 
 import torch
@@ -41,6 +42,12 @@
 NOT_APPLICABLE = "n/a"
 
 
+class ModelSummaryTrainingMode(Enum):
+    TRAIN = "train"
+    EVAL = "eval"
+    FREEZE = "freeze"
+
+
 class LayerSummary:
     """Summary class for a single layer in a :class:`~lightning.pytorch.core.LightningModule`. It collects the
     following information:
@@ -146,6 +153,13 @@ def training(self) -> bool:
         """Returns whether the module is in training mode."""
         return self._module.training
 
+    @property
+    def requires_grad(self) -> bool:
+        """Returns whether the module requires grad."""
+        if self.num_parameters > 0:
+            return any(param.requires_grad for name, param in self._module.named_parameters())
+        return True
+
 
 class ModelSummary:
     """Generates a summary of all layers in a :class:`~lightning.pytorch.core.LightningModule`.
@@ -281,14 +295,32 @@ def param_nums(self) -> list[int]:
         return [layer.num_parameters for layer in self._layer_summary.values()]
 
     @property
-    def training_modes(self) -> list[bool]:
-        return [layer.training for layer in self._layer_summary.values()]
+    def training_modes(self) -> list[int]:
+        return [
+            (ModelSummaryTrainingMode.TRAIN if layer.training else ModelSummaryTrainingMode.EVAL)
+            if layer.requires_grad
+            else ModelSummaryTrainingMode.FREEZE
+            for layer in self._layer_summary.values()
+        ]
 
     @property
     def total_training_modes(self) -> dict[str, int]:
         modes = [layer.training for layer in self._model.modules()]
         modes = modes[1:]  # exclude the root module
         return {"train": modes.count(True), "eval": modes.count(False)}
+        # modes = [
+        #     (
+        #         (ModelSummaryTrainingMode.TRAIN if layer.training else ModelSummaryTrainingMode.EVAL)
+        #         if any(p.requires_grad for p in layer.parameters())
+        #         else ModelSummaryTrainingMode.FREEZE
+        #     ).value
+        #     for layer in islice(self._model.modules(), 1, None)  # exclude the root module
+        # ]
+        # return {
+        #     "train": modes.count(ModelSummaryTrainingMode.TRAIN.value),
+        #     "eval": modes.count(ModelSummaryTrainingMode.EVAL.value),
+        #     "freeze": modes.count(ModelSummaryTrainingMode.FREEZE.value),
+        # }
 
     @property
     def total_parameters(self) -> int:
@@ -382,7 +414,7 @@ def _get_summary_data(self) -> list[tuple[str, list[str]]]:
             ("Name", self.layer_names),
             ("Type", self.layer_types),
             ("Params", list(map(get_human_readable_count, self.param_nums))),
-            ("Mode", ["train" if mode else "eval" for mode in self.training_modes]),
+            ("Mode", [mode.value for mode in self.training_modes]),
             ("FLOPs", list(map(get_human_readable_count, (sum(x.values()) for x in self.flop_counts.values())))),
         ]
         if self._model.example_input_array is not None:
@@ -487,6 +519,8 @@ def _format_summary_table(
     summary += "Modules in train mode"
     summary += "\n" + s.format(total_training_modes["eval"], 10)
     summary += "Modules in eval mode"
+    # summary += "\n" + s.format(total_training_modes["freeze"], 10)
+    # summary += "Modules in freeze mode"
     summary += "\n" + s.format(get_human_readable_count(total_flops), 10)
     summary += "Total Flops"