diff --git a/train.py b/train.py
index 3c74bdfe..ea1a6034 100644
--- a/train.py
+++ b/train.py
@@ -100,19 +100,23 @@ def train(
         else:
             cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
 
-    # Scheduler (reduce lr at epochs 218, 245, i.e. batches 400k, 450k)
+    # Scheduler https://github.com/ultralytics/yolov3/issues/238
     # lf = lambda x: 1 - x / epochs  # linear ramp to zero
-    # lf = lambda x: 10 ** (-2 * x / epochs)  # exp ramp to lr0 * 1e-2
-    lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs))  # inv exp ramp to lr0 * 1e-2
+    # lf = lambda x: 10 ** (hyp['lrf'] * x / epochs)  # exp ramp to lr0 * hyp['lrf']
+    lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs))  # inv exp ramp to lr0 * hyp['lrf']
     scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
-    # scheduler = optim.lr_scheduler.MultiStepLR(optimizer,milestones=[218, 245],gamma=0.1,last_epoch=start_epoch - 1)
+    # scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch - 1)
 
-    # Plot lr schedule
+    # # Plot lr schedule
     # y = []
     # for _ in range(epochs):
     #     scheduler.step()
     #     y.append(optimizer.param_groups[0]['lr'])
-    # plt.plot(y)
+    # plt.plot(y, label='LambdaLR')
+    # plt.xlabel('epoch')
+    # plt.xlabel('LR')
+    # plt.tight_layout()
+    # plt.savefig('LR.png', dpi=300)
 
     # Dataset
     dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
@@ -165,8 +169,6 @@ def train(
             imgs = imgs.to(device)
             targets = targets.to(device)
             nt = len(targets)
-            # if nt == 0:  # if no targets continue
-            #     continue
 
             # Plot images with bounding boxes
             if epoch == 0 and i == 0: