diff --git a/README.md b/README.md
index 10c4638..8a014a1 100644
--- a/README.md
+++ b/README.md
@@ -134,6 +134,12 @@ You can train your **YOLO-NAS** model with **Single Command Line**
 ```
 python3 train.py --data /dir/dataset/data.yaml --batch 6 --epoch 100 --model yolo_nas_m --size 640
 ```
+### If your training ends in 65th epoch (total 100 epochs), now you can start from 65th epoch and complete your 100 epochs training.
+**Example:**
+```
+python3 train.py --data /dir/dataset/data.yaml --batch 6 --epoch 100 --model yolo_nas_m --size 640 \
+                 --weight runs/train2/ckpt_latest.pth --resume
+```
 
 ## 📺 Inference
 You can Inference your **YOLO-NAS** model with **Single Command Line**
diff --git a/train.py b/train.py
index 896d093..27f43b0 100644
--- a/train.py
+++ b/train.py
@@ -60,20 +60,25 @@
 
     s_time = time.time()
 
+    
     if args['name'] is None:
         name = 'train'
     else:
         name = args['name']
-    n = 0
-    while True:
-        if not os.path.exists(os.path.join('runs', f'{name}{n}')):
-            name = f'{name}{n}'
-            os.makedirs(os.path.join('runs', name))
-            print(f"[INFO] Checkpoints saved in \033[1m{os.path.join('runs', name)}\033[0m")
-            break
-        else:
-            n += 1
-
+    
+    if args['resume']:
+        name = os.path.split(args['weight'])[0].split('/')[-1]
+    else:
+        n = 0
+        while True:
+            if not os.path.exists(os.path.join('runs', f'{name}{n}')):
+                name = f'{name}{n}'
+                os.makedirs(os.path.join('runs', name))
+                break
+            else:
+                n += 1
+                
+    print(f"[INFO] Checkpoints saved in \033[1m{os.path.join('runs', name)}\033[0m")
     # Training on GPU or CPU
     if args['cpu']:
         print('[INFO] Training on \033[1mCPU\033[0m')
@@ -184,6 +189,13 @@
         "metric_to_watch": 'mAP@0.50'
     }
 
+    # to Resume Training
+    if args['resume']:
+        train_params['resume'] = True
+    
+    # Print Training Params
+    print('[INFO] Training Params:\n', train_params)
+
     trainer.train(
         model=model, 
         training_params=train_params,