diff --git a/nbs/04_learner.ipynb b/nbs/04_learner.ipynb
index dea9cdb..4fdc5a8 100755
--- a/nbs/04_learner.ipynb
+++ b/nbs/04_learner.ipynb
@@ -1452,7 +1452,30 @@
     "        if self.log_model:\n",
     "            torch.save(learn.model.state_dict(), self.model_name)\n",
     "            wandb.save(self.model_name)\n",
-    "        self.run.finish()\n"
+    "        self.run.finish()\n",
+    "\n",
+    "\n",
+    "class EarlyStoppingCallback(Callback):\n",
+    "    order = MetricsCB.order+1\n",
+    "    def __init__(self, patience=3, min_delta=0.001):\n",
+    "        self.patience = patience\n",
+    "        self.min_delta = min_delta\n",
+    "        self.best_loss = float('inf')\n",
+    "        self.wait = 0\n",
+    "        \n",
+    "    def after_epoch(self, learn):\n",
+    "        if not learn.model.training:\n",
+    "            current_loss = learn.metrics.all_metrics['loss'].compute()\n",
+    "            if current_loss < self.best_loss - self.min_delta:\n",
+    "                self.best_loss = current_loss\n",
+    "                self.wait = 0\n",
+    "            else:\n",
+    "                self.wait += 1\n",
+    "            \n",
+    "            if self.wait >= self.patience:\n",
+    "                print(\"Early stopping triggered.\")\n",
+    "                raise CancelFitException\n",
+    "\n"
    ]
   },
   {
diff --git a/rapidai/_modidx.py b/rapidai/_modidx.py
index f229b37..a8c3ed2 100644
--- a/rapidai/_modidx.py
+++ b/rapidai/_modidx.py
@@ -165,6 +165,11 @@
                                  'rapidai.learner.DeviceCB.__init__': ('learner.html#devicecb.__init__', 'rapidai/learner.py'),
                                  'rapidai.learner.DeviceCB.before_batch': ('learner.html#devicecb.before_batch', 'rapidai/learner.py'),
                                  'rapidai.learner.DeviceCB.before_fit': ('learner.html#devicecb.before_fit', 'rapidai/learner.py'),
+                                 'rapidai.learner.EarlyStoppingCallback': ('learner.html#earlystoppingcallback', 'rapidai/learner.py'),
+                                 'rapidai.learner.EarlyStoppingCallback.__init__': ( 'learner.html#earlystoppingcallback.__init__',
+                                                                                     'rapidai/learner.py'),
+                                 'rapidai.learner.EarlyStoppingCallback.after_epoch': ( 'learner.html#earlystoppingcallback.after_epoch',
+                                                                                        'rapidai/learner.py'),
                                  'rapidai.learner.LRFinderCB': ('learner.html#lrfindercb', 'rapidai/learner.py'),
                                  'rapidai.learner.LRFinderCB.__init__': ('learner.html#lrfindercb.__init__', 'rapidai/learner.py'),
                                  'rapidai.learner.LRFinderCB.after_batch': ('learner.html#lrfindercb.after_batch', 'rapidai/learner.py'),
diff --git a/rapidai/learner.py b/rapidai/learner.py
index 486539c..7f657bb 100755
--- a/rapidai/learner.py
+++ b/rapidai/learner.py
@@ -3,7 +3,7 @@
 # %% auto 0
 __all__ = ['CancelFitException', 'CancelBatchException', 'CancelEpochException', 'Callback', 'run_cbs', 'SingleBatchCB', 'to_cpu',
            'MetricsCB', 'DeviceCB', 'TrainCB', 'ProgressCB', 'with_cbs', 'Learner', 'TrainLearner', 'MomentumLearner',
-           'LRFinderCB', 'lr_find', 'WandbCB']
+           'LRFinderCB', 'lr_find', 'WandbCB', 'EarlyStoppingCallback']
 
 # %% ../nbs/04_learner.ipynb 1
 import math,torch,matplotlib.pyplot as plt
@@ -279,3 +279,26 @@ def cleanup_fit(self, learn):
             wandb.save(self.model_name)
         self.run.finish()
 
+
+class EarlyStoppingCallback(Callback):
+    order = MetricsCB.order+1
+    def __init__(self, patience=3, min_delta=0.001):
+        self.patience = patience
+        self.min_delta = min_delta
+        self.best_loss = float('inf')
+        self.wait = 0
+        
+    def after_epoch(self, learn):
+        if not learn.model.training:
+            current_loss = learn.metrics.all_metrics['loss'].compute()
+            if current_loss < self.best_loss - self.min_delta:
+                self.best_loss = current_loss
+                self.wait = 0
+            else:
+                self.wait += 1
+            
+            if self.wait >= self.patience:
+                print("Early stopping triggered.")
+                raise CancelFitException
+
+