diff --git a/README.md b/README.md
index c14979d..55e602a 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,8 @@
 # Exercise and practice when I'm learning :open_book:
 这个项目用于记录我读研期间的一些练习，作为我的练兵场，记录我练习技能的场所！
-:fire: :fire: :fire:
\ No newline at end of file
+:fire: :fire: :fire:
+
+现在已经有的内容：
+- [通用指标的计算](./general_metrics.py)，实现了混淆矩阵和混淆矩阵可视化
+- [语义分割指标的计算](./segmentation_metrics.py)，只实现了 dice 系数和 IoU 的计算
+- 
\ No newline at end of file
diff --git a/general_metrics.py b/general_metrics.py
new file mode 100644
index 0000000..dc30051
--- /dev/null
+++ b/general_metrics.py
@@ -0,0 +1,63 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def confusion_matrix(label, predict, n):
+    """
+    计算混淆矩阵
+    :param label: 标签，np.array类型。形状可以是(n_sample,) 或者 (n_sample, n_classes)，当为第二种形状时可以表示多标签分类的情况
+    :param predict: 预测值，与 `label` 同理
+    :param n: 类别数目
+    :return: 混淆矩阵，np.array类型。shape 为 (n, n)。$cm_{ij}$表示真实标签为 $i$，预测标签为 $j$ 的样本个数
+    """
+    k = (label >= 0) & (label < n)
+    # bincount()函数用于统计数组内每个非负整数的个数
+    # 详见 https://docs.scipy.org/doc/numpy/reference/generated/numpy.bincount.html
+    return np.bincount(n * label[k].astype(int) + predict[k], minlength=n ** 2).reshape(n, n)
+
+
+def plot_confusion_matrix(cm, classes, normalized=False, title="Confusion matrix", cmap=plt.cm.Blues):
+    """
+    画混淆矩阵
+    :param cm: 混淆矩阵
+    :param classes: 类别列表。classes[i] 表示 i 所对应的类名
+    :param normalized: 是否进行归一化
+    :param title: str，表头
+    :param cmap: 配色方案
+    :return:
+    """
+    if normalized:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+        print("Normalized confusion matrix")
+    else:
+        print("Confusion matrix with out normalization")
+
+    print(cm)
+
+    plt.imshow(cm, interpolation="nearest", cmap=cmap)
+    plt.title(title, fontsize=14)
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+
+    fmt = '.2f' if normalized else 'd'
+    thresh = cm.max() / 2.
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+        plt.text(j, i, format(cm[i, j], fmt),
+                 horizontalalignment="center",
+                 color="white" if cm[i, j] > thresh else "black")
+    plt.tight_layout()
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+    plt.show()
+    
+
+if __name__ == "__main__":
+    label = np.array([2, 0, 1, 1])  # np.array([[0, 1, 1], [1, 1, 0], [0, 0, 1]])
+    predict = np.array([0, 0, 1, 1])  # np.array([[0, 1, 0], [1, 0, 1], [0, 0, 0]])
+    cm = confusion_matrix(label, predict, 3)
+    plot_confusion_matrix(cm, [0, 1, 2], title="Confusion Matrix")
+    print(f"Confusion Matrix: \n{cm}")
+
+    print(f"mIoU: {IoU(label, predict, 3)}")
diff --git a/segmentation_metrics.py b/segmentation_metrics.py
index 5371b50..6284ba4 100644
--- a/segmentation_metrics.py
+++ b/segmentation_metrics.py
@@ -2,24 +2,9 @@
 图像分割的评价指标
 """
 import numpy as np
-import matplotlib.pyplot as plt
 import itertools
 
 
-def confusion_matrix(label, predict, n):
-    """
-    计算混淆矩阵
-    :param label: 标签，np.array类型。形状可以是(n_sample,) 或者 (n_sample, n_classes)，当为第二种形状时可以表示多标签分类的情况
-    :param predict: 预测值，与 `label` 同理
-    :param n: 类别数目
-    :return: 混淆矩阵，np.array类型。shape 为 (n, n)。$cm_{ij}$表示真实标签为 $i$，预测标签为 $j$ 的样本个数
-    """
-    k = (label >= 0) & (label < n)
-    # bincount()函数用于统计数组内每个非负整数的个数
-    # 详见 https://docs.scipy.org/doc/numpy/reference/generated/numpy.bincount.html
-    return np.bincount(n * label[k].astype(int) + predict[k], minlength=n ** 2).reshape(n, n)
-
-
 def IoU(label, predict, class_n):
     """
     计算各类的IoU， Intersection over Union
@@ -39,43 +24,6 @@ def IoU(label, predict, class_n):
     return miou
 
 
-def plot_confusion_matrix(cm, classes, normalized=False, title="Confusion matrix", cmap=plt.cm.Blues):
-    """
-    画混淆矩阵
-    :param cm: 混淆矩阵
-    :param classes: 类别列表。classes[i] 表示 i 所对应的类名
-    :param normalized: 是否进行归一化
-    :param title: str，表头
-    :param cmap: 配色方案
-    :return:
-    """
-    if normalized:
-        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
-        print("Normalized confusion matrix")
-    else:
-        print("Confusion matrix with out normalization")
-
-    print(cm)
-
-    plt.imshow(cm, interpolation="nearest", cmap=cmap)
-    plt.title(title, fontsize=14)
-    plt.colorbar()
-    tick_marks = np.arange(len(classes))
-    plt.xticks(tick_marks, classes, rotation=45)
-    plt.yticks(tick_marks, classes)
-
-    fmt = '.2f' if normalized else 'd'
-    thresh = cm.max() / 2.
-    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
-        plt.text(j, i, format(cm[i, j], fmt),
-                 horizontalalignment="center",
-                 color="white" if cm[i, j] > thresh else "black")
-    plt.tight_layout()
-    plt.ylabel('True label')
-    plt.xlabel('Predicted label')
-    plt.show()
-
-
 def dice_coefficient(pred, target, smooth=1):
     """
     计算dice系数
@@ -92,13 +40,3 @@ def dice_coefficient(pred, target, smooth=1):
     intersection = (t1 * t2).sum()
 
     return 2. * intersection / (t1.sum() + t2.sum() + smooth)
-
-
-if __name__ == "__main__":
-    label = np.array([2, 0, 1, 1])  # np.array([[0, 1, 1], [1, 1, 0], [0, 0, 1]])
-    predict = np.array([0, 0, 1, 1])  # np.array([[0, 1, 0], [1, 0, 1], [0, 0, 0]])
-    cm = confusion_matrix(label, predict, 3)
-    plot_confusion_matrix(cm, [0, 1, 2], title="Confusion Matrix")
-    print(f"Confusion Matrix: \n{cm}")
-
-    print(f"mIoU: {IoU(label, predict, 3)}")