diff --git a/callback.py b/callback.py
new file mode 100644
index 0000000..6fed415
--- /dev/null
+++ b/callback.py
@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+# @Brief: lr的相关回调函数
+
+import math
+import tensorflow as tf
+from tensorflow.keras import callbacks, backend, optimizers
+import numpy as np
+
+
+class CosineAnnealingLRScheduler(optimizers.schedules.LearningRateSchedule):
+    def __init__(self, epochs, train_step, lr_max, lr_min, warmth_rate=0.2):
+        super(CosineAnnealingLRScheduler, self).__init__()
+        self.total_step = epochs * train_step
+        self.warm_step = int(self.total_step * warmth_rate)
+        self.lr_max = lr_max
+        self.lr_min = lr_min
+
+    @tf.function
+    def __call__(self, step):
+        if step < self.warm_step:
+            lr = self.lr_max / self.warm_step * step
+        else:
+            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (1.0 + tf.cos((step - self.warm_step) / self.total_step * np.pi))
+
+        return lr
+
+
+def print_lr(optimizer):
+    def lr(y_true, y_pred):
+        return optimizer._decayed_lr("float32")
+    return lr
diff --git a/config.py b/config.py
new file mode 100644
index 0000000..7e1423f
--- /dev/null
+++ b/config.py
@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+# @Brief: 配置文件
+
+input_shape = (320, 320, 3)
+num_classes = 21
+
+epochs = 100
+batch_size = 4
+lr = 0.0001
+
+train_txt_path = "D:/Code/Data/VOC2012/ImageSets/SegmentationAug/train.txt"
+val_txt_path = "D:/Code/Data/VOC2012/ImageSets/SegmentationAug/val.txt"
+trainval_txt_path = "D:/Code/Data/VOC2012/ImageSets/SegmentationAug/trainval.txt"
diff --git a/dataset.py b/dataset.py
new file mode 100644
index 0000000..d4425c6
--- /dev/null
+++ b/dataset.py
@@ -0,0 +1,78 @@
+# -*- coding: utf-8 -*-
+# @Brief: 不同数据集的父类实现
+
+import numpy as np
+import glob
+from PIL import Image
+import cv2 as cv
+import os
+import core.config as cfg
+
+
+class Dataset:
+    def __init__(self, target_size=(320, 320), num_classes=21):
+        self.target_size = target_size
+        self.num_classes = num_classes
+
+    def set_image_info(self, **kwargs):
+        """
+        Dataset的基类，需要实现self.image_info来存储输入图像的路径和对应mask路径
+        :return: None
+        """
+        pass
+
+    def read_mask(self, image_id):
+        """
+        读取mask，并转换成语义分割需要的结构。
+        :param image_id: 图像的id号
+        :return: mask
+        """
+        mask_path = self.image_info[image_id]["mask_path"]
+        image = Image.open(mask_path)
+        image = np.array(image)
+
+        image = resize_image_with_pad(image, self.target_size, pad_value=0.0)
+
+        # 转为one hot形式的标签
+        h, w = self.target_size
+        mask = np.zeros((h, w, self.num_classes), np.uint8)
+
+        for c in range(1, self.num_classes):
+            m = np.argwhere(image == c)
+
+            for row, col in m:
+                mask[row, col, c] = 1
+
+        return mask
+
+
+def resize_image_with_pad(image, target_size, pad_value=128.0):
+    """
+    resize图像，多余的地方用其他颜色填充
+    :param image: 输入图像
+    :param target_size: resize后图像的大小
+    :param pad_value: 填充区域像素值
+    :return: image_padded
+    """
+    image_h, image_w = image.shape[:2]
+    input_h, input_w = target_size
+
+    scale = min(input_h / image_h, input_w / image_w)
+
+    image_h = int(image_h * scale)
+    image_w = int(image_w * scale)
+
+    dw, dh = (input_w - image_w) // 2, (input_h - image_h) // 2
+
+    if pad_value == 0:
+        # mask 用最近领域插值
+        image_resize = cv.resize(image, (image_w, image_h), interpolation=cv.INTER_NEAREST)
+        image_padded = np.full(shape=[input_h, input_w], fill_value=pad_value)
+        image_padded[dh: image_h+dh, dw: image_w+dw] = image_resize
+    else:
+        # image 用双线性插值
+        image_resize = cv.resize(image, (image_w, image_h), interpolation=cv.INTER_LINEAR)
+        image_padded = np.full(shape=[input_h, input_w, 3], fill_value=pad_value)
+        image_padded[dh: image_h+dh, dw: image_w+dw, :] = image_resize
+
+    return image_padded