diff --git a/train.py b/train.py
index a6c34cbc466c..aaee41a499e4 100644
--- a/train.py
+++ b/train.py
@@ -265,21 +265,13 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         model.train()
 
-        # Update image weights (optional)
+        # Update image weights (optional, single-GPU only)
         if opt.image_weights:
-            # Generate indices
-            if RANK in [-1, 0]:
-                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
-                iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
-                dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
-            # Broadcast if DDP
-            if RANK != -1:
-                indices = (torch.tensor(dataset.indices) if RANK == 0 else torch.zeros(dataset.n)).int()
-                dist.broadcast(indices, 0)
-                if RANK != 0:
-                    dataset.indices = indices.cpu().numpy()
-
-        # Update mosaic border
+            cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
+            iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
+            dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
+
+        # Update mosaic border (optional)
         # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
         # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders