diff --git a/dataset.py b/dataset.py
new file mode 100644
index 0000000..0da2166
--- /dev/null
+++ b/dataset.py
@@ -0,0 +1,56 @@
+import torch
+import cv2
+import os
+import glob
+from torch.utils.data import Dataset
+import random
+import matplotlib.pyplot as plt
+
+class ISBI_Loader(Dataset):
+    def __init__(self, data_path):
+
+        # 初始化函数，读取所有data_path下的图片
+        self.data_path = data_path
+        self.imgs_path = glob.glob(os.path.join(data_path, '*.png'))
+
+    def augment(self, image, flipCode):
+        # 使用cv2.flip进行数据增强，filpCode为1水平翻转，0垂直翻转，-1水平+垂直翻转
+        flip = cv2.flip(image, flipCode)
+        return flip
+
+    def __getitem__(self, index):
+        # 根据index读取图片
+        image_path = self.imgs_path[index]
+        # 根据image_path生成label_path
+        label_path = image_path.replace('normal_jpg', 'jpg')
+        # 读取训练图片和标签图片
+        image = cv2.imread(image_path)
+        label = cv2.imread(label_path)
+        # 将数据转为单通道的图片
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        label = cv2.cvtColor(label, cv2.COLOR_BGR2GRAY)
+        image = image.reshape(1, image.shape[0], image.shape[1])
+        label = label.reshape(1, label.shape[0], label.shape[1])
+        # 处理标签，将像素值为255的改为1
+        if label.max() > 1:
+            label = label / 255
+        # 随机进行数据增强，为2时不做处理
+        flipCode = random.choice([-1, 0, 1, 2])
+        if flipCode != 2:
+            image = self.augment(image, flipCode)
+            label = self.augment(label, flipCode)
+        return image, label
+
+    def __len__(self):
+        # 返回训练集大小
+        return len(self.imgs_path)
+
+
+if __name__ == "__main__":
+    isbi_dataset = ISBI_Loader(r'F:/unet_program/H/normal_jpg/')
+    print("数据个数：", len(isbi_dataset))
+    train_loader = torch.utils.data.DataLoader(dataset=isbi_dataset,
+                                               batch_size=2,
+                                               shuffle=True)
+    for image, label in train_loader:
+        print(image.shape)