diff --git a/train.py b/train.py
index 77a5d434..c0880862 100644
--- a/train.py
+++ b/train.py
@@ -193,10 +193,10 @@ def train(
                 if int(name.split('.')[1]) < cutoff:  # if layer < 75
                     p.requires_grad = False if epoch == 0 else True
 
-        # Update image weights (optional)
-        w = model.class_weights.cpu().numpy() * (1 - maps)  # class weights
-        image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
-        dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n)  # random weighted index
+        # # Update image weights (optional)
+        # w = model.class_weights.cpu().numpy() * (1 - maps)  # class weights
+        # image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
+        # dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n)  # random weighted index
 
         mloss = torch.zeros(5).to(device)  # mean losses
         for i, (imgs, targets, _, _) in enumerate(dataloader):
diff --git a/utils/utils.py b/utils/utils.py
index 6a4e90e0..83f7fdf9 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -543,7 +543,6 @@ def kmeans_targets(path='./data/coco_64img.txt'):  # from utils.utils import *;
 
 
 # Plotting functions ---------------------------------------------------------------------------------------------------
-
 def plot_one_box(x, img, color=None, label=None, line_thickness=None):
     # Plots one bounding box on image img
     tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1  # line thickness