diff --git a/detect.py b/detect.py
index 2fb96eaa59a1..9d922aed1d12 100644
--- a/detect.py
+++ b/detect.py
@@ -46,7 +46,7 @@ def detect(save_img=False):
         dataset = LoadImages(source, img_size=imgsz)
 
     # Get names and colors
-    names = model.names if hasattr(model, 'names') else model.modules.names
+    names = model.module.names if hasattr(model, 'module') else model.names
     colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
 
     # Run inference
diff --git a/train.py b/train.py
index d9d2884d6e97..ea72f1549f9a 100644
--- a/train.py
+++ b/train.py
@@ -79,6 +79,7 @@ def train(hyp):
     # Create model
     model = Model(opt.cfg).to(device)
     assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (opt.data, nc, opt.cfg, model.md['nc'])
+    model.names = data_dict['names']
 
     # Image sizes
     gs = int(max(model.stride))  # grid size (max stride)
@@ -193,7 +194,6 @@ def train(hyp):
     model.hyp = hyp  # attach hyperparameters to model
     model.gr = 1.0  # giou loss ratio (obj_loss = 1.0 or giou)
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
-    model.names = data_dict['names']
 
     # Class frequency
     labels = np.concatenate(dataset.labels, 0)