diff --git a/detectron2/engine/hooks.py b/detectron2/engine/hooks.py
index fc37af0fd3..26bc045d9f 100644
--- a/detectron2/engine/hooks.py
+++ b/detectron2/engine/hooks.py
@@ -222,6 +222,7 @@ def __init__(
         val_metric: str,
         mode: str = "max",
         file_prefix: str = "model_best",
+        patience: int = None,
     ) -> None:
         """
         Args:
@@ -231,10 +232,12 @@ def __init__(
             mode (str): one of {'max', 'min'}. controls whether the chosen val metric should be
                 maximized or minimized, e.g. for "bbox/AP50" it should be "max"
             file_prefix (str): the prefix of checkpoint's filename, defaults to "model_best"
+            patience (int): the number of evaluation cycles without improvement before early stopping
         """
         self._logger = logging.getLogger(__name__)
         self._period = eval_period
         self._val_metric = val_metric
+        self._patience = patience 
         assert mode in [
             "max",
             "min",
@@ -297,6 +300,24 @@ def after_step(self):
             and next_iter != self.trainer.max_iter
         ):
             self._best_checking()
+            
+            if self._patience is None or self.best_iter is None:
+                return
+            
+            iterations_without_improvement = (self.trainer.iter-self.best_iter) // self._period
+
+            if(iterations_without_improvement > self._patience): 
+                self._logger.info(
+                    f"Early stopping triggered at iteration {self.trainer.iter} due to lack of improvement " 
+                    f"after {iterations_without_improvement} cycles."
+                )                
+                raise Exception("Early stopping triggered. Terminating training process.")
+
+            if(iterations_without_improvement > 0):
+                self._logger.info(
+                    f"No improvement detected in the last {iterations_without_improvement} evaluation cycles. " 
+                    f"{self._patience - iterations_without_improvement} cycles remain before early stopping."
+                )
 
     def after_train(self):
         # same conditions as `EvalHook`