diff --git a/train.py b/train.py
index 085b689..baaa717 100644
--- a/train.py
+++ b/train.py
@@ -33,18 +33,18 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
     start_time=time.time()
     first_iter = 0
     tb_writer = prepare_output_and_logger(dataset)  # Tensorboard writer
-    gaussians = GaussianModel(dataset.sh_degree)    #高斯模型
-    scene = Scene(dataset, gaussians)             #场景
-    gaussians.training_setup(opt)           #训练设置
+    gaussians = GaussianModel(dataset.sh_degree)    
+    scene = Scene(dataset, gaussians)             
+    gaussians.training_setup(opt)           
     if checkpoint:
         (model_params, first_iter) = torch.load(checkpoint)
         gaussians.restore(model_params, opt)
 
-    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0] #背景颜色
-    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda") #背景颜色
+    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0] 
+    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda") 
 
-    iter_start = torch.cuda.Event(enable_timing = True)  #开始时间
-    iter_end = torch.cuda.Event(enable_timing = True)    #结束时间
+    iter_start = torch.cuda.Event(enable_timing = True)  
+    iter_end = torch.cuda.Event(enable_timing = True)    
 
     viewpoint_stack = None
     ema_loss_for_log = 0.0
@@ -66,9 +66,9 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
             except Exception as e:
                 network_gui.conn = None
 
-        iter_start.record()  #记录开始时间
+        iter_start.record()  
 
-        gaussians.update_learning_rate(iteration)   #更新学习率
+        gaussians.update_learning_rate(iteration)   
 
         # Every 1000 its we increase the levels of SH up to a maximum degree
         if iteration % 1000 == 0:
@@ -85,7 +85,7 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
 
         bg = torch.rand((3), device="cuda") if opt.random_background else background
 
-        render_pkg = render(viewpoint_cam, gaussians, pipe, bg)  #渲染
+        render_pkg = render(viewpoint_cam, gaussians, pipe, bg)  
         image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
         
         # Loss
@@ -141,9 +141,9 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
                 print("\n[ITER {}] Saving Checkpoint".format(iteration))
                 torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
 
-    end_time = time.time()  # 记录训练结束时间
-    total_time = end_time - start_time  # 计算总时间
-    print(f"\nTraining complete. Total training time: {total_time:.2f} seconds.")  # 打印总时间
+    end_time = time.time()  
+    total_time = end_time - start_time  
+    print(f"\nTraining complete. Total training time: {total_time:.2f} seconds.")  
 
 
 def prepare_output_and_logger(args):