diff --git a/best_checkpoint_copier/__init__.py b/best_checkpoint_copier/__init__.py
index 337c7b6..c710f8f 100644
--- a/best_checkpoint_copier/__init__.py
+++ b/best_checkpoint_copier/__init__.py
@@ -3,6 +3,7 @@
 import shutil
 import tensorflow as tf
 
+
 class Checkpoint(object):
   dir = None
   file = None
@@ -25,7 +26,13 @@ class BestCheckpointCopier(tf.estimator.Exporter):
   sort_key_fn = None
   sort_reverse = None
 
-  def __init__(self, name='best_checkpoints', checkpoints_to_keep=5, score_metric='Loss/total_loss', compare_fn=lambda x,y: x.score < y.score, sort_key_fn=lambda x: x.score, sort_reverse=False):
+  def __init__(self, name='best_checkpoints', 
+               checkpoints_to_keep=5, 
+               score_metric='Loss/total_loss', 
+               compare_fn=lambda x,y: x.score < y.score, 
+               sort_key_fn=lambda x: x.score, 
+               sort_reverse=False,
+               patience=25):
     self.checkpoints = []
     self.checkpoints_to_keep = checkpoints_to_keep
     self.compare_fn = compare_fn
@@ -33,6 +40,8 @@ def __init__(self, name='best_checkpoints', checkpoints_to_keep=5, score_metric=
     self.score_metric = score_metric
     self.sort_key_fn = sort_key_fn
     self.sort_reverse = sort_reverse
+    self.early_stop_patient = 0
+    self.patience = patience
     super(BestCheckpointCopier, self).__init__()
 
   def _copyCheckpoint(self, checkpoint):
@@ -40,9 +49,13 @@ def _copyCheckpoint(self, checkpoint):
     os.makedirs(desination_dir, exist_ok=True)
 
     for file in glob.glob(r'{}*'.format(checkpoint.path)):
+      if not self.name in desination_dir: desination_dir = os.path.join(desination_dir, self.name)
       self._log('copying {} to {}'.format(file, desination_dir))
       shutil.copy(file, desination_dir)
 
+    with open(desination_dir+"/meta.txt", "a") as f:
+      f.write(f"{file.split('/')[-1]}   {round(checkpoint.score, 5)}\n")
+
   def _destinationDir(self, checkpoint):
     return os.path.join(checkpoint.dir, self.name)
 
@@ -53,6 +66,7 @@ def _keepCheckpoint(self, checkpoint):
     self.checkpoints = sorted(self.checkpoints, key=self.sort_key_fn, reverse=self.sort_reverse)
 
     self._copyCheckpoint(checkpoint)
+    self.early_stop_patient = 0
 
   def _log(self, statement):
     tf.logging.info('[{}] {}'.format(self.__class__.__name__, statement))
@@ -83,7 +97,10 @@ def export(self, estimator, export_path, checkpoint_path, eval_result, is_the_fi
     checkpoint = Checkpoint(path=checkpoint_path, score=score)
 
     if self._shouldKeep(checkpoint):
-      self._keepCheckpoint(checkpoint)
       self._pruneCheckpoints(checkpoint)
+      self._keepCheckpoint(checkpoint)
     else:
       self._log('skipping checkpoint {}'.format(checkpoint.path))
+      self.early_stop_patient += 1
+      if self.early_stop_patient > self.patience:
+          raise ValueError (f"Stopping training. mAP@.50 didn't improve in last {self.patience} epochs.")