diff --git a/Dataloader.py b/Dataloader.py
index fab833b..7d9ec3e 100644
--- a/Dataloader.py
+++ b/Dataloader.py
@@ -15,7 +15,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 
-def create_dataloaders(data_root='..',
+def create_dataloaders(data_root='../trash_division_data/ultimate_4_class/',
                        batch_size=32,
                        image_size=256,
                        val_split=0.2,
@@ -111,9 +111,8 @@ def create_dataloaders(data_root='..',
     )
 
     # 4. 获取类别名称
-    class_names = train_dataset.classes if hasattr(train_dataset, 'classes') else ['0', '1', '2', '3']
+    class_names = train_dataset.classes
     print(f"类别: {class_names}")
-    print(f"类别映射: {train_dataset.class_to_idx if hasattr(train_dataset, 'class_to_idx') else '0-3'}")
 
     return train_loader, val_loader, class_names
 
@@ -158,7 +157,7 @@ def visualize_batch(dataloader, class_names, num_images=8):
 
 if __name__ == '__main__':
     train_loader, val_loader, class_names = create_dataloaders(
-        data_root='..',  # 与trash-division同级文件夹
+        data_root='../trash_division_data/ultimate_4_class/',  # 与trash-division同级文件夹
         batch_size=32,  # 根据你的显存调整
         image_size=256,  # 与你模型输入一致
         num_workers=4,  # Windows 可能需设为 0
diff --git a/merge_classes.py b/Merge_classes.py
similarity index 90%
rename from merge_classes.py
rename to Merge_classes.py
index a605aa2..145c345 100644
--- a/merge_classes.py
+++ b/Merge_classes.py
@@ -1,5 +1,5 @@
 """将原数据集合并为我们需要的四个大类
-    运行时先配置路径
+    已修改成相对路径 具体配置方法详见README.md
 
     author：
     weikaiwen
@@ -18,9 +18,9 @@ import shutil
 
 # ================= 1. 配置你的路径 =================
 # 注意：请确保相对路径正确，以下为示例
-ORIGINAL_DATA_DIR = '/Users/weikaiwen/Desktop/trash_division_data'           # 原始数据集的目录
-NEW_DATA_DIR = '/Users/weikaiwen/Desktop/trash_division_data/ultimate_4_class'         # 合并后的新目录
-CLASSNAME_FILE = '/Users/weikaiwen/Desktop/trash_division_data/val/classname.txt' # txt 文件的位置
+ORIGINAL_DATA_DIR = '../trash_division_data'           # 原始数据集的目录
+NEW_DATA_DIR = '../trash_division_data/ultimate_4_class'         # 合并后的新目录
+CLASSNAME_FILE = '../trash_division_data/val/classname.txt' # txt 文件的位置
 # ===================================================
 
 
diff --git a/Train.py b/Train.py
index 6c87c69..e5d462c 100644
--- a/Train.py
+++ b/Train.py
@@ -12,7 +12,7 @@ import torch.optim as optim
 from tqdm import tqdm  # 进度条，可选
 import matplotlib.pyplot as plt
 from Model import Net
-
+from Dataloader import create_dataloaders
 def train_one_epoch(model, train_loader, criterion, optimizer, device, epoch):
     """训练一个epoch"""
     model.train()  # 设置为训练模式
@@ -171,11 +171,16 @@ def plot_training_history(history):
 # ========== 使用示例 ==========
 if __name__ == '__main__':
     # 假设你的 dataloader 已经写好了
-    # train_loader = ...
-    # val_loader = ...
+    train_loader, val_loader, class_names = create_dataloaders(
+        data_root='../trash_division_data/ultimate_4_class/',  # 与trash-division同级文件夹
+        batch_size=32,  # 根据你的显存调整
+        image_size=256,  # 与你模型输入一致
+        num_workers=4,  # Windows 可能需设为 0
+        augment=True  # 训练时使用数据增强
+    )
 
     # 1. 创建模型
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    device = torch.device('cuda' if torch.cuda.is_available() else 'xpu' if torch.xpu.is_available() else 'cpu')
     model = Net().get_network()  # 根据你的 Net 类调整
     model = model.to(device)