diff --git a/model/IFNet.py b/model/IFNet.py
index eff6c21..6ba503d 100644
--- a/model/IFNet.py
+++ b/model/IFNet.py
@@ -99,7 +99,7 @@ class IFNet(nn.Module):
             merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
             if gt.shape[1] == 3:
                 loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach()
-                loss_distill += ((flow_teacher.detach() - flow_list[i]).abs() * loss_mask).mean()
+                loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5) * loss_mask).mean()
         c0 = self.contextnet(img0, flow[:, :2])
         c1 = self.contextnet(img1, flow[:, 2:4])
         tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
diff --git a/model/IFNet_m.py b/model/IFNet_m.py
index d42e32c..29bed16 100644
--- a/model/IFNet_m.py
+++ b/model/IFNet_m.py
@@ -100,7 +100,7 @@ class IFNet_m(nn.Module):
             merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
             if gt.shape[1] == 3:
                 loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach()
-                loss_distill += ((flow_teacher.detach() - flow_list[i]).abs() * loss_mask).mean()
+                loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5) * loss_mask).mean()
         c0 = self.contextnet(img0, flow[:, :2])
         c1 = self.contextnet(img1, flow[:, 2:4])
         tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
diff --git a/train.py b/train.py
index 93a4dc4..5f67dda 100644
--- a/train.py
+++ b/train.py
@@ -21,10 +21,10 @@ log_path = 'train_log'
 def get_learning_rate(step):
     if step < 2000:
         mul = step / 2000.
-        return 3e-4 * mul
+        return 1e-4 * mul
     else:
         mul = np.cos((step - 2000) / (args.epoch * args.step_per_epoch - 2000.) * math.pi) * 0.5 + 0.5
-        return (3e-4 - 3e-5) * mul + 3e-5
+        return (1e-4 - 1e-5) * mul + 1e-5
 
 def flow2rgb(flow_map_np):
     h, w, _ = flow_map_np.shape