diff --git a/paddleseg/core/train.py b/paddleseg/core/train.py
index 33459db7f6..b12a2f08ea 100644
--- a/paddleseg/core/train.py
+++ b/paddleseg/core/train.py
@@ -65,6 +65,7 @@ def train(model,
           save_dir='output',
           iters=10000,
           batch_size=2,
+          early_stop_interval=None,
           resume_model=None,
           save_interval=1000,
           log_iters=10,
@@ -117,6 +118,8 @@ def train(model,
     local_rank = paddle.distributed.ParallelEnv().local_rank
 
     start_iter = 0
+    stop_count = 0
+    stop_status = False
     if resume_model is not None:
         start_iter = resume(model, optimizer, resume_model)
 
@@ -171,7 +174,7 @@ def train(model,
     save_models = deque()
     batch_start = time.time()
     iter = start_iter
-    while iter < iters:
+    while iter < iters and not stop_status:
         if iter == start_iter and use_ema:
             init_ema_params(ema_model, model)
         for data in loader:
@@ -354,15 +357,26 @@ def train(model,
 
                 if val_dataset is not None:
                     if mean_iou > best_mean_iou:
+                        stop_count = 0
                         best_mean_iou = mean_iou
                         best_model_iter = iter
                         best_model_dir = os.path.join(save_dir, "best_model")
                         paddle.save(
                             model.state_dict(),
                             os.path.join(best_model_dir, 'model.pdparams'))
-                    logger.info(
-                        '[EVAL] The model with the best validation mIoU ({:.4f}) was saved at iter {}.'
-                        .format(best_mean_iou, best_model_iter))
+                    elif mean_iou < best_mean_iou:
+                        stop_count += 1
+
+                    if early_stop_interval is not None and stop_count >= early_stop_interval:
+                        stop_status = True
+                        logger.info(
+                            'Early stopping at iter {}. The best mean IoU is {:.4f}.'
+                            .format(iter, best_mean_iou))
+                    else:
+                        logger.info(
+                            '[EVAL] The model with the best validation mIoU ({:.4f}) was saved at iter {}.'
+                            .format(best_mean_iou, best_model_iter))
+
                     if use_ema:
                         if ema_mean_iou > best_ema_mean_iou:
                             best_ema_mean_iou = ema_mean_iou
@@ -385,6 +399,10 @@ def train(model,
                                                   ema_mean_iou, iter)
                             log_writer.add_scalar('Evaluate/Ema_Acc', ema_acc,
                                                   iter)
+
+                    if stop_status:
+                        break
+
             batch_start = time.time()
 
     # Calculate flops.
diff --git a/tools/train.py b/tools/train.py
index b9ce6cf7af..b28651035b 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -82,6 +82,11 @@ def parse_args():
         help='Maximum number of checkpoints to save.',
         type=int,
         default=5)
+    parser.add_argument(
+        '--early_stop_intervals',
+        help='Early Stop at args number of save intervals.',
+        type=None,
+        default=0)
 
     # Other params
     parser.add_argument(
@@ -187,6 +192,7 @@ def main(args):
         save_dir=args.save_dir,
         iters=cfg.iters,
         batch_size=cfg.batch_size,
+        early_stop=args.args.early_stop_intervals,
         resume_model=args.resume_model,
         save_interval=args.save_interval,
         log_iters=args.log_iters,