diff --git a/.DS_Store b/.DS_Store
index 4f6eca8..ec1d9d8 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0608b9b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+# cache
+.DS_Store
+*/.DS_Store
+.vscode/
+*/__pycache__
+
+# Pytorch
+*.pth
+*.py~
+*.sh~
\ No newline at end of file
diff --git a/1.png b/1.png
new file mode 100644
index 0000000..55b256c
Binary files /dev/null and b/1.png differ
diff --git a/BaseML/Classification.py b/BaseML/Classification.py
index 75fa2b5..a5b57f3 100644
--- a/BaseML/Classification.py
+++ b/BaseML/Classification.py
@@ -1,18 +1,26 @@
 import pandas as pd
 import numpy as np
 import os
-from sklearn.metrics import accuracy_score, mean_squared_error
+import matplotlib.pyplot as plt
+import seaborn
+from sklearn.metrics import accuracy_score, mean_squared_error, precision_score, recall_score, confusion_matrix
 from sklearn.naive_bayes import GaussianNB
 from sklearn.ensemble import AdaBoostClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.neural_network import MLPClassifier
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
 from sklearn.cluster import KMeans
 from sklearn.metrics import silhouette_score, silhouette_samples
 import joblib
+import cv2
 import random
+from skimage.feature import hog
+from scipy.spatial.distance import cdist
+
+from demo.face import extract_hog_features
 
 class cls:
     def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS=5):
@@ -23,6 +31,8 @@ def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS=
         self.test_size = 0.2
         self.test_set = ' '
         self.x_train, self.x_test, self.y_train, self.y_test = 0, 0, 0, 0
+        self.X = 0
+        self.Y = 0
         if self.algorithm == 'KNN':
             self.model = KNeighborsClassifier(n_neighbors=n_neighbors)
         elif self.algorithm == 'SVM':
@@ -38,6 +48,8 @@ def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS=
             self.model = KMeans(self.n)
         elif self.algorithm == 'MLP':
             self.model = MLPClassifier(solver='lbfgs')
+        elif self.algorithm == 'RandomForest':
+            self.model == RandomForestClassifier(n_estimators=n_estimators,random_state=0)
 
     def train(self, seed=0, data_type='csv'):
         if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP']:
@@ -72,14 +84,19 @@ def train(self, seed=0, data_type='csv'):
 
             self.x_train = np.delete(self.x_train, delete_list, axis=1)
             self.model.fit(self.x_train)
+        
+        elif self.algorithm == 'RandomForest':
+            self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.X, self.Y, test_size=0.25, random_state=42)
+            self.model.fit(self.x_train, self.y_train)
+
 
     def inference(self, data = np.nan):
         if data is not np.nan: # 对data进行了指定
             self.x_test = data
 
-        if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP','KNN','CART']:
-            pred = self.model.predict(self.x_test)
-            return pred
+        if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP','KNN','CART', 'RandomForest']:
+            self.pred = self.model.predict(self.x_test)
+            return self.pred
 
         elif self.algorithm == 'Kmeans':
             labels = self.model.labels_      # 获取聚类标签
@@ -166,3 +183,98 @@ def get_data(self,X,y,x_column,y_column):
             self.y_train = y[:,y_column]
             if self.y_train.shape[0]:
                 self.dataset = np.concatenate((self.x_train,self.y_train),axis=1) # 按列进行拼接
+
+    def extract_hog_features(self, X):
+        image_descriptors = []
+        for i in range(len(X)):                                         # 此处的X为之前训练部分所有图像的矩阵形式拼接而来，
+            # print(i)                                                  # 所以len(X)实为X中矩阵的个数，即训练部分图像的个数
+            fd, _ = hog(X[i], orientations=9, pixels_per_cell=(16, 16), cells_per_block=(16, 16),
+                                block_norm='L2-Hys', visualize=True)    # 此处的参数细节详见其他文章
+            image_descriptors.append(fd)                                # 拼接得到所有图像的hog特征
+        return image_descriptors                                        # 返回的是训练部分所有图像的hog特征
+
+
+    def extract_hog_features_single(self, X):
+        image_descriptors_single = []
+        fd, _ = hog(X, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(16, 16),
+                                block_norm='L2-Hys', visualize=True)
+        image_descriptors_single.append(fd)
+        return image_descriptors_single
+
+    
+    def read_data_and_pre(self, label2id, path):
+        X = []
+        Y = []
+        path =r'../dataset/JAFFE/training_set'
+        for label in os.listdir(path):
+            for img_file in os.listdir(os.path.join(path, label)):              # 遍历
+                image = cv2.imread(os.path.join(path, label, img_file))         # 读取图像
+                result = image/255.0                                            # 图像归一化
+                res = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)                   # 转灰度
+                # cv2.waitKey(0)
+                cv2.destroyAllWindows()
+                X.append(res)                                                   # 将读取到的所有图像的矩阵形式拼接在一起
+                Y.append(label2id[label])                                       # 将读取到的所有图像的标签拼接在一起
+        self.X = self.extract_hog_features(X)
+        self.Y = Y
+        return self.extract_hog_features(X), Y                                                             # 返回的X,Y分别是图像的矩阵表达和图像的标签
+
+
+    def plot(self):
+        acc = accuracy_score(self.y_test, self.pred)
+        precision = precision_score(self.y_test, self.pred, average='macro')
+        recall = recall_score(self.y_test, self.pred, average='macro')
+        cm = confusion_matrix(self.y_test, self.pred)
+        print(cm)
+        print('Acc: ', acc)
+        print('Precision: ', precision)
+        print('Recall: ', recall)
+        
+        xtick = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+        ytick = xtick
+        
+        f, ax = plt.subplots(figsize=(7, 5))
+        ax.tick_params(axis='y', labelsize=15)
+        ax.tick_params(axis='x', labelsize=15)
+        
+        seaborn.set(font_scale=1.2)
+        plt.rc('font',family='Times New Roman', size=15)
+        
+        seaborn.heatmap(cm,fmt='g', cmap='Blues', annot=True, cbar=True,xticklabels=xtick, yticklabels=ytick, ax=ax)
+        
+        plt.title('Confusion Matrix', fontsize='x-large')
+        
+        plt.show()
+
+    
+    def test(self, path):
+    # 下面为同一文件夹下多张图片的表情识别
+    # labelid2 = {0:'angry',1: 'disgust',2: 'fear',3:'happy',4:'neutral',5:'sad',6:'surprise'}
+        path=path
+        i = 1
+        for dir in os.listdir((path)):
+            temp_path = os.path.join(path, dir)
+            for image_file in os.listdir(temp_path):
+                image = cv2.imread(os.path.join(temp_path,image_file))
+                # result = image/255.0
+                # cv2.waitKey(1000)
+                # cv2.destroyAllWindows()
+                result = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+                X_Single = self.extract_hog_features_single(result)
+                predict = self.model.predict(X_Single)         # 可以在这里选择分类器的类别
+                print(i)
+                i += 1
+                if predict == 0:
+                    print('angry')
+                elif predict == 1:
+                    print('disgust')
+                elif predict == 2:
+                    print('fear')
+                elif predict == 3:
+                    print('happy')
+                elif predict == 4:
+                    print('neutral')
+                elif predict == 5:
+                    print('sad')
+                elif predict == 6:
+                    print('surprise')
diff --git a/BaseNN/__init__.py b/BaseNN/__init__.py
new file mode 100644
index 0000000..6965f89
--- /dev/null
+++ b/BaseNN/__init__.py
@@ -0,0 +1 @@
+from .ops import *
diff --git a/BaseNN/models/1.log b/BaseNN/models/1.log
new file mode 100644
index 0000000..4406be1
--- /dev/null
+++ b/BaseNN/models/1.log
@@ -0,0 +1,274 @@
+MMDataParallel(
+  (module): Detector(
+    (backbone): ResNetWrapper(
+      (model): ResNet(
+        (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (relu): ReLU(inplace=True)
+        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+        (layer1): Sequential(
+          (0): BasicBlock(
+            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (1): BasicBlock(
+            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (2): BasicBlock(
+            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+        (layer2): Sequential(
+          (0): BasicBlock(
+            (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (downsample): Sequential(
+              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
+              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            )
+          )
+          (1): BasicBlock(
+            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (2): BasicBlock(
+            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (3): BasicBlock(
+            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+        (layer3): Sequential(
+          (0): BasicBlock(
+            (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (downsample): Sequential(
+              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
+              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            )
+          )
+          (1): BasicBlock(
+            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (2): BasicBlock(
+            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (3): BasicBlock(
+            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (4): BasicBlock(
+            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (5): BasicBlock(
+            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+        (layer4): Sequential(
+          (0): BasicBlock(
+            (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (downsample): Sequential(
+              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
+              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            )
+          )
+          (1): BasicBlock(
+            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (2): BasicBlock(
+            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (relu): ReLU(inplace=True)
+            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+        )
+      )
+    )
+    (neck): SFPN(
+      (lateral_convs): ModuleList(
+        (0): ConvModule(
+          (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
+        )
+        (1): ConvModule(
+          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
+        )
+        (2): ConvModule(
+          (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
+        )
+      )
+      (fpn_convs): ModuleList(
+        (0): ConvModule(
+          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+        (1): ConvModule(
+          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+        (2): ConvModule(
+          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        )
+      )
+      (sfpn_conv1): SFMBlock1(
+        (upsample): UpsamplingBilinear2d(scale_factor=2.0, mode=bilinear)
+        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
+        (conv_block): ConvBlock(
+          (conv1): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=64)
+          (conv2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
+          (conv3): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
+          (norm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (ac): GELU()
+        )
+      )
+      (sfpn_conv2): SFMBlock2(
+        (upsample): UpsamplingBilinear2d(scale_factor=2.0, mode=bilinear)
+        (dwsample): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
+        (conv_block): ConvBlock(
+          (conv1): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=64)
+          (conv2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
+          (conv3): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
+          (norm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (ac): GELU()
+        )
+      )
+      (sfpn_conv3): SFMBlock3(
+        (dwsample): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
+        (conv_block): ConvBlock(
+          (conv1): Conv2d(64, 64, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=64)
+          (conv2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
+          (conv3): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
+          (norm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (ac): GELU()
+        )
+      )
+    )
+    (heads): CLRHead(
+      (prior_embeddings): Embedding(192, 3)
+      (seg_decoder): SegDecoder(
+        (dropout): Dropout2d(p=0.1, inplace=False)
+        (conv): Conv2d(192, 7, kernel_size=(1, 1), stride=(1, 1))
+      )
+      (reg_modules): ModuleList(
+        (0): Linear(in_features=64, out_features=64, bias=True)
+        (1): ReLU(inplace=True)
+        (2): Linear(in_features=64, out_features=64, bias=True)
+        (3): ReLU(inplace=True)
+      )
+      (cls_modules): ModuleList(
+        (0): Linear(in_features=64, out_features=64, bias=True)
+        (1): ReLU(inplace=True)
+        (2): Linear(in_features=64, out_features=64, bias=True)
+        (3): ReLU(inplace=True)
+      )
+      (roi_gather): ROIGather(
+        (f_key): ConvModule(
+          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (activate): ReLU(inplace=True)
+        )
+        (f_query): Sequential(
+          (0): Conv1d(192, 192, kernel_size=(1,), stride=(1,), groups=192)
+          (1): ReLU()
+        )
+        (f_value): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
+        (W): Conv1d(192, 192, kernel_size=(1,), stride=(1,), groups=192)
+        (resize): FeatureResize()
+        (convs): ModuleList(
+          (0): ConvModule(
+            (conv): Conv2d(64, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)
+            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (activate): ReLU(inplace=True)
+          )
+          (1): ConvModule(
+            (conv): Conv2d(64, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)
+            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (activate): ReLU(inplace=True)
+          )
+          (2): ConvModule(
+            (conv): Conv2d(64, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)
+            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (activate): ReLU(inplace=True)
+          )
+        )
+        (catconv): ModuleList(
+          (0): ConvModule(
+            (conv): Conv2d(48, 64, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)
+            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (activate): ReLU(inplace=True)
+          )
+          (1): ConvModule(
+            (conv): Conv2d(96, 64, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)
+            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (activate): ReLU(inplace=True)
+          )
+          (2): ConvModule(
+            (conv): Conv2d(144, 64, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False)
+            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (activate): ReLU(inplace=True)
+          )
+        )
+        (fc): Linear(in_features=2304, out_features=64, bias=True)
+        (fc_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
+      )
+      (reg_layers): Linear(in_features=64, out_features=76, bias=True)
+      (cls_layers): Linear(in_features=64, out_features=2, bias=True)
+      (criterion): NLLLoss()
+    )
+  )
+)
\ No newline at end of file
diff --git a/BaseNN/models/__init__.py b/BaseNN/models/__init__.py
new file mode 100644
index 0000000..2193d57
--- /dev/null
+++ b/BaseNN/models/__init__.py
@@ -0,0 +1,46 @@
+from BaseNN.utils import Registry, build_from_cfg
+import torch.nn as nn
+
+BACKBONES = Registry('backbones')
+AGGREGATORS = Registry('aggregators')
+HEADS = Registry('heads')
+NECKS = Registry('necks')
+NETS = Registry('nets')
+
+
+def build(cfg, registry, default_args=None):
+    if isinstance(cfg, list):
+        modules = [
+            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
+        ]
+        return nn.Sequential(*modules)
+    else:
+        return build_from_cfg(cfg, registry, default_args)
+
+
+def build_backbones(cfg):
+    return build(cfg.backbone, BACKBONES, default_args=dict(cfg=cfg))
+
+
+def build_necks(cfg):
+    return build(cfg.necks, NECKS, default_args=dict(cfg=cfg))
+
+
+def build_aggregator(cfg):
+    return build(cfg.aggregator, AGGREGATORS, default_args=dict(cfg=cfg))
+
+
+def build_heads(cfg):
+    return build(cfg.heads, HEADS, default_args=dict(cfg=cfg))
+
+
+def build_head(split_cfg, cfg):
+    return build(split_cfg, HEADS, default_args=dict(cfg=cfg))
+
+
+def build_net(cfg):
+    return build(cfg.net, NETS, default_args=dict(cfg=cfg))
+
+
+def build_necks(cfg):
+    return build(cfg.neck, NECKS, default_args=dict(cfg=cfg))
diff --git a/BaseNN/models/backbbone/eduNet.py b/BaseNN/models/backbbone/eduNet.py
new file mode 100644
index 0000000..f262e8e
--- /dev/null
+++ b/BaseNN/models/backbbone/eduNet.py
@@ -0,0 +1,43 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from cnblock import CNBlock
+# from torch.hub import load_state_dict_from_url
+
+from models.registry import BACKBONES
+
+@BACKBONES.register_module
+class eduNet(nn.Module):
+    def __init__(self,
+                 resnet='resnet18',
+                 pretrained=True,
+                 replace_stride_with_dilation=[False, False, False],
+                 out_conv=False,
+                 fea_stride=8,
+                 out_channel=128,
+                 in_channels=[64, 128, 256, 512],
+                 cfg=None):
+        super(eduNet, self).__init__()
+        self.cfg = cfg
+        self.in_channels = in_channels
+        self.cnblock = CNBlock(64, 512, 3, 1, 1)
+
+        self.model = eval(resnet)(
+            pretrained=pretrained,
+            replace_stride_with_dilation=replace_stride_with_dilation,
+            in_channels=self.in_channels)
+        self.out = None
+        if out_conv:
+            out_channel = 512
+            for chan in reversed(self.in_channels):
+                if chan < 0: continue
+                out_channel = chan
+                break
+            self.out = self.cnblock(out_channel * self.model.expansion,
+                               cfg.featuremap_out_channel)
+
+    def forward(self, x):
+        x = self.model(x)
+        if self.out:
+            x[-1] = self.out(x[-1])
+        return x
\ No newline at end of file
diff --git a/BaseNN/models/cnblock.py b/BaseNN/models/cnblock.py
new file mode 100644
index 0000000..a11ba6a
--- /dev/null
+++ b/BaseNN/models/cnblock.py
@@ -0,0 +1,40 @@
+from turtle import forward
+from pyparsing import Forward
+import torch.nn as nn
+import torch
+from mmcv.cnn import ConvModule
+
+class CNBlock(nn.Module):
+    def __init__(self,
+                in_dim,
+                out_dim,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                norm_cfg=dict(type='SyncBN'),
+                ac_cfg=dict(type='ReLU'),
+                groups=0,
+                dilation=0) -> None:
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+        self.norm = norm_cfg
+        self.ac = ac_cfg
+        self.group = groups
+        self.dilation = dilation
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+
+        self.cnblock = ConvModule(self.in_dim,
+                                self.out_dim,
+                                kernel_size=self.kernel_size,
+                                stride=self.stride,
+                                padding=self.padding,
+                                norm_cfg = self.norm,
+                                ac_cfg = self.ac)
+    
+    def forward(self, input):
+        res = self.cnblock(input)
+
+        return res
diff --git a/BaseNN/models/registry.py b/BaseNN/models/registry.py
new file mode 100644
index 0000000..65a2386
--- /dev/null
+++ b/BaseNN/models/registry.py
@@ -0,0 +1,46 @@
+from clrnet.utils import Registry, build_from_cfg
+import torch.nn as nn
+
+BACKBONES = Registry('backbones')
+AGGREGATORS = Registry('aggregators')
+HEADS = Registry('heads')
+NECKS = Registry('necks')
+NETS = Registry('nets')
+
+
+def build(cfg, registry, default_args=None):
+    if isinstance(cfg, list):
+        modules = [
+            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
+        ]
+        return nn.Sequential(*modules)
+    else:
+        return build_from_cfg(cfg, registry, default_args)
+
+
+def build_backbones(cfg):
+    return build(cfg.backbone, BACKBONES, default_args=dict(cfg=cfg))
+
+
+def build_necks(cfg):
+    return build(cfg.necks, NECKS, default_args=dict(cfg=cfg))
+
+
+def build_aggregator(cfg):
+    return build(cfg.aggregator, AGGREGATORS, default_args=dict(cfg=cfg))
+
+
+def build_heads(cfg):
+    return build(cfg.heads, HEADS, default_args=dict(cfg=cfg))
+
+
+def build_head(split_cfg, cfg):
+    return build(split_cfg, HEADS, default_args=dict(cfg=cfg))
+
+
+def build_net(cfg):
+    return build(cfg.net, NETS, default_args=dict(cfg=cfg))
+
+
+def build_necks(cfg):
+    return build(cfg.neck, NECKS, default_args=dict(cfg=cfg))
diff --git a/BaseNN/utils/__init__.py b/BaseNN/utils/__init__.py
new file mode 100644
index 0000000..eb99ab0
--- /dev/null
+++ b/BaseNN/utils/__init__.py
@@ -0,0 +1,2 @@
+from .config import Config
+from .registry import Registry, build_from_cfg
diff --git a/BaseNN/utils/config.py b/BaseNN/utils/config.py
new file mode 100644
index 0000000..1ef3747
--- /dev/null
+++ b/BaseNN/utils/config.py
@@ -0,0 +1,418 @@
+# Copyright (c) Open-MMLab. All rights reserved.
+import ast
+import os.path as osp
+import shutil
+import sys
+import tempfile
+from argparse import Action, ArgumentParser
+from collections import abc
+from importlib import import_module
+
+from addict import Dict
+from yapf.yapflib.yapf_api import FormatCode
+
+BASE_KEY = '_base_'
+DELETE_KEY = '_delete_'
+RESERVED_KEYS = ['filename', 'text', 'pretty_text']
+
+
+def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
+    if not osp.isfile(filename):
+        raise FileNotFoundError(msg_tmpl.format(filename))
+
+
+class ConfigDict(Dict):
+    def __missing__(self, name):
+        raise KeyError(name)
+
+    def __getattr__(self, name):
+        try:
+            value = super(ConfigDict, self).__getattr__(name)
+        except KeyError:
+            ex = AttributeError(f"'{self.__class__.__name__}' object has no "
+                                f"attribute '{name}'")
+        except Exception as e:
+            ex = e
+        else:
+            return value
+        raise ex
+
+
+def add_args(parser, cfg, prefix=''):
+    for k, v in cfg.items():
+        if isinstance(v, str):
+            parser.add_argument('--' + prefix + k)
+        elif isinstance(v, int):
+            parser.add_argument('--' + prefix + k, type=int)
+        elif isinstance(v, float):
+            parser.add_argument('--' + prefix + k, type=float)
+        elif isinstance(v, bool):
+            parser.add_argument('--' + prefix + k, action='store_true')
+        elif isinstance(v, dict):
+            add_args(parser, v, prefix + k + '.')
+        elif isinstance(v, abc.Iterable):
+            parser.add_argument('--' + prefix + k, type=type(v[0]), nargs='+')
+        else:
+            print(f'cannot parse key {prefix + k} of type {type(v)}')
+    return parser
+
+
+class Config:
+    """A facility for config and config files.
+    It supports common file formats as configs: python/json/yaml. The interface
+    is the same as a dict object and also allows access config values as
+    attributes.
+    Example:
+        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
+        >>> cfg.a
+        1
+        >>> cfg.b
+        {'b1': [0, 1]}
+        >>> cfg.b.b1
+        [0, 1]
+        >>> cfg = Config.fromfile('tests/data/config/a.py')
+        >>> cfg.filename
+        "/home/kchen/projects/mmcv/tests/data/config/a.py"
+        >>> cfg.item4
+        'test'
+        >>> cfg
+        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
+        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
+    """
+    @staticmethod
+    def _validate_py_syntax(filename):
+        with open(filename) as f:
+            content = f.read()
+        try:
+            ast.parse(content)
+        except SyntaxError:
+            raise SyntaxError('There are syntax errors in config '
+                              f'file {filename}')
+
+    @staticmethod
+    def _file2dict(filename):
+        filename = osp.abspath(osp.expanduser(filename))
+        check_file_exist(filename)
+        if filename.endswith('.py'):
+            with tempfile.TemporaryDirectory() as temp_config_dir:
+                temp_config_file = tempfile.NamedTemporaryFile(
+                    dir=temp_config_dir, suffix='.py')
+                temp_config_name = osp.basename(temp_config_file.name)
+                shutil.copyfile(filename,
+                                osp.join(temp_config_dir, temp_config_name))
+                temp_module_name = osp.splitext(temp_config_name)[0]
+                sys.path.insert(0, temp_config_dir)
+                Config._validate_py_syntax(filename)
+                mod = import_module(temp_module_name)
+                sys.path.pop(0)
+                cfg_dict = {
+                    name: value
+                    for name, value in mod.__dict__.items()
+                    if not name.startswith('__')
+                }
+                # delete imported module
+                del sys.modules[temp_module_name]
+                # close temp file
+                temp_config_file.close()
+        elif filename.endswith(('.yml', '.yaml', '.json')):
+            import mmcv
+            cfg_dict = mmcv.load(filename)
+        else:
+            raise IOError('Only py/yml/yaml/json type are supported now!')
+
+        cfg_text = ''
+        with open(filename, 'r') as f:
+            cfg_text += f.read()
+
+        if BASE_KEY in cfg_dict:
+            cfg_dir = osp.dirname(filename)
+            base_filename = cfg_dict.pop(BASE_KEY)
+            base_filename = base_filename if isinstance(
+                base_filename, list) else [base_filename]
+
+            cfg_dict_list = list()
+            cfg_text_list = list()
+            for f in base_filename:
+                _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f))
+                cfg_dict_list.append(_cfg_dict)
+                cfg_text_list.append(_cfg_text)
+
+            base_cfg_dict = dict()
+            for c in cfg_dict_list:
+                if len(base_cfg_dict.keys() & c.keys()) > 0:
+                    raise KeyError('Duplicate key is not allowed among bases')
+                base_cfg_dict.update(c)
+
+            base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict)
+            cfg_dict = base_cfg_dict
+
+            # merge cfg_text
+            cfg_text_list.append(cfg_text)
+            cfg_text = '\n'.join(cfg_text_list)
+
+        return cfg_dict, cfg_text
+
+    @staticmethod
+    def _merge_a_into_b(a, b):
+        # merge dict `a` into dict `b` (non-inplace). values in `a` will
+        # overwrite `b`.
+        # copy first to avoid inplace modification
+        b = b.copy()
+        for k, v in a.items():
+            if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
+                if not isinstance(b[k], dict):
+                    raise TypeError(
+                        f'{k}={v} in child config cannot inherit from base '
+                        f'because {k} is a dict in the child config but is of '
+                        f'type {type(b[k])} in base config. You may set '
+                        f'`{DELETE_KEY}=True` to ignore the base config')
+                b[k] = Config._merge_a_into_b(v, b[k])
+            else:
+                b[k] = v
+        return b
+
+    @staticmethod
+    def fromfile(filename):
+        cfg_dict, cfg_text = Config._file2dict(filename)
+        return Config(cfg_dict, cfg_text=cfg_text, filename=filename)
+
+    @staticmethod
+    def auto_argparser(description=None):
+        """Generate argparser from config file automatically (experimental)
+        """
+        partial_parser = ArgumentParser(description=description)
+        partial_parser.add_argument('config', help='config file path')
+        cfg_file = partial_parser.parse_known_args()[0].config
+        cfg = Config.fromfile(cfg_file)
+        parser = ArgumentParser(description=description)
+        parser.add_argument('config', help='config file path')
+        add_args(parser, cfg)
+        return parser, cfg
+
+    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
+        if cfg_dict is None:
+            cfg_dict = dict()
+        elif not isinstance(cfg_dict, dict):
+            raise TypeError('cfg_dict must be a dict, but '
+                            f'got {type(cfg_dict)}')
+        for key in cfg_dict:
+            if key in RESERVED_KEYS:
+                raise KeyError(f'{key} is reserved for config file')
+
+        super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict))
+        super(Config, self).__setattr__('_filename', filename)
+        if cfg_text:
+            text = cfg_text
+        elif filename:
+            with open(filename, 'r') as f:
+                text = f.read()
+        else:
+            text = ''
+        super(Config, self).__setattr__('_text', text)
+
+    @property
+    def filename(self):
+        return self._filename
+
+    @property
+    def text(self):
+        return self._text
+
+    @property
+    def pretty_text(self):
+
+        indent = 4
+
+        def _indent(s_, num_spaces):
+            s = s_.split('\n')
+            if len(s) == 1:
+                return s_
+            first = s.pop(0)
+            s = [(num_spaces * ' ') + line for line in s]
+            s = '\n'.join(s)
+            s = first + '\n' + s
+            return s
+
+        def _format_basic_types(k, v, use_mapping=False):
+            if isinstance(v, str):
+                v_str = f"'{v}'"
+            else:
+                v_str = str(v)
+
+            if use_mapping:
+                k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                attr_str = f'{k_str}: {v_str}'
+            else:
+                attr_str = f'{str(k)}={v_str}'
+            attr_str = _indent(attr_str, indent)
+
+            return attr_str
+
+        def _format_list(k, v, use_mapping=False):
+            # check if all items in the list are dict
+            if all(isinstance(_, dict) for _ in v):
+                v_str = '[\n'
+                v_str += '\n'.join(
+                    f'dict({_indent(_format_dict(v_), indent)}),'
+                    for v_ in v).rstrip(',')
+                if use_mapping:
+                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                    attr_str = f'{k_str}: {v_str}'
+                else:
+                    attr_str = f'{str(k)}={v_str}'
+                attr_str = _indent(attr_str, indent) + ']'
+            else:
+                attr_str = _format_basic_types(k, v, use_mapping)
+            return attr_str
+
+        def _contain_invalid_identifier(dict_str):
+            contain_invalid_identifier = False
+            for key_name in dict_str:
+                contain_invalid_identifier |= \
+                    (not str(key_name).isidentifier())
+            return contain_invalid_identifier
+
+        def _format_dict(input_dict, outest_level=False):
+            r = ''
+            s = []
+
+            use_mapping = _contain_invalid_identifier(input_dict)
+            if use_mapping:
+                r += '{'
+            for idx, (k, v) in enumerate(input_dict.items()):
+                is_last = idx >= len(input_dict) - 1
+                end = '' if outest_level or is_last else ','
+                if isinstance(v, dict):
+                    v_str = '\n' + _format_dict(v)
+                    if use_mapping:
+                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                        attr_str = f'{k_str}: dict({v_str}'
+                    else:
+                        attr_str = f'{str(k)}=dict({v_str}'
+                    attr_str = _indent(attr_str, indent) + ')' + end
+                elif isinstance(v, list):
+                    attr_str = _format_list(k, v, use_mapping) + end
+                else:
+                    attr_str = _format_basic_types(k, v, use_mapping) + end
+
+                s.append(attr_str)
+            r += '\n'.join(s)
+            if use_mapping:
+                r += '}'
+            return r
+
+        cfg_dict = self._cfg_dict.to_dict()
+        text = _format_dict(cfg_dict, outest_level=True)
+        # copied from setup.cfg
+        yapf_style = dict(based_on_style='pep8',
+                          blank_line_before_nested_class_or_def=True,
+                          split_before_expression_after_opening_paren=True)
+        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
+
+        return text
+
+    def __repr__(self):
+        return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}'
+
+    def __len__(self):
+        return len(self._cfg_dict)
+
+    def __getattr__(self, name):
+        return getattr(self._cfg_dict, name)
+
+    def __getitem__(self, name):
+        return self._cfg_dict.__getitem__(name)
+
+    def __setattr__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setattr__(name, value)
+
+    def __setitem__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setitem__(name, value)
+
+    def __iter__(self):
+        return iter(self._cfg_dict)
+
+    def haskey(self, name):
+        return hasattr(self._cfg_dict, name)
+
+    def dump(self, file=None):
+        cfg_dict = super(Config, self).__getattribute__('_cfg_dict').to_dict()
+        if self.filename.endswith('.py'):
+            if file is None:
+                return self.pretty_text
+            else:
+                with open(file, 'w') as f:
+                    f.write(self.pretty_text)
+        else:
+            import mmcv
+            if file is None:
+                file_format = self.filename.split('.')[-1]
+                return mmcv.dump(cfg_dict, file_format=file_format)
+            else:
+                mmcv.dump(cfg_dict, file)
+
+    def has_attr_in_cfg(self, name):
+        return hasattr(self._cfg_dict, name)
+
+    def merge_from_dict(self, options):
+        """Merge list into cfg_dict
+        Merge the dict parsed by MultipleKVAction into this cfg.
+        Examples:
+            >>> options = {'model.backbone.depth': 50,
+            ...            'model.backbone.with_cp':True}
+            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
+            >>> cfg.merge_from_dict(options)
+            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+            >>> assert cfg_dict == dict(
+            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
+        Args:
+            options (dict): dict of configs to merge from.
+        """
+        option_cfg_dict = {}
+        for full_key, v in options.items():
+            d = option_cfg_dict
+            key_list = full_key.split('.')
+            for subkey in key_list[:-1]:
+                d.setdefault(subkey, ConfigDict())
+                d = d[subkey]
+            subkey = key_list[-1]
+            d[subkey] = v
+
+        cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+        super(Config, self).__setattr__(
+            '_cfg_dict', Config._merge_a_into_b(option_cfg_dict, cfg_dict))
+
+
+class DictAction(Action):
+    """
+    argparse action to split an argument into KEY=VALUE form
+    on the first = and append to a dictionary. List options should
+    be passed as comma separated values, i.e KEY=V1,V2,V3
+    """
+    @staticmethod
+    def _parse_int_float_bool(val):
+        try:
+            return int(val)
+        except ValueError:
+            pass
+        try:
+            return float(val)
+        except ValueError:
+            pass
+        if val.lower() in ['true', 'false']:
+            return True if val.lower() == 'true' else False
+        return val
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        options = {}
+        for kv in values:
+            key, val = kv.split('=', maxsplit=1)
+            val = [self._parse_int_float_bool(v) for v in val.split(',')]
+            if len(val) == 1:
+                val = val[0]
+            options[key] = val
+        setattr(namespace, self.dest, options)
diff --git a/BaseNN/utils/registry.py b/BaseNN/utils/registry.py
new file mode 100644
index 0000000..54ba615
--- /dev/null
+++ b/BaseNN/utils/registry.py
@@ -0,0 +1,82 @@
+import inspect
+
+import six
+
+# borrow from mmdetection
+
+
+def is_str(x):
+    """Whether the input is an string instance."""
+    return isinstance(x, six.string_types)
+
+
+class Registry(object):
+    def __init__(self, name):
+        self._name = name
+        self._module_dict = dict()
+
+    def __repr__(self):
+        format_str = self.__class__.__name__ + '(name={}, items={})'.format(
+            self._name, list(self._module_dict.keys()))
+        return format_str
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def module_dict(self):
+        return self._module_dict
+
+    def get(self, key):
+        return self._module_dict.get(key, None)
+
+    def _register_module(self, module_class):
+        """Register a module.
+
+        Args:
+            module (:obj:`nn.Module`): Module to be registered.
+        """
+        if not inspect.isclass(module_class):
+            raise TypeError('module must be a class, but got {}'.format(
+                type(module_class)))
+        module_name = module_class.__name__
+        if module_name in self._module_dict:
+            raise KeyError('{} is already registered in {}'.format(
+                module_name, self.name))
+        self._module_dict[module_name] = module_class
+
+    def register_module(self, cls):
+        self._register_module(cls)
+        return cls
+
+
+def build_from_cfg(cfg, registry, default_args=None):
+    """Build a module from config dict.
+
+    Args:
+        cfg (dict): Config dict. It should at least contain the key "type".
+        registry (:obj:`Registry`): The registry to search the type from.
+        default_args (dict, optional): Default initialization arguments.
+
+    Returns:
+        obj: The constructed object.
+    """
+    assert isinstance(cfg, dict) and 'type' in cfg
+    assert isinstance(default_args, dict) or default_args is None
+    args = cfg.copy()
+    obj_type = args.pop('type')
+    if is_str(obj_type):
+        obj_cls = registry.get(obj_type)
+        if obj_cls is None:
+            raise KeyError('{} is not in the {} registry'.format(
+                obj_type, registry.name))
+    elif inspect.isclass(obj_type):
+        obj_cls = obj_type
+    else:
+        raise TypeError('type must be a str or valid type, but got {}'.format(
+            type(obj_type)))
+    if default_args is not None:
+        for name, value in default_args.items():
+            args.setdefault(name, value)
+    return obj_cls(**args)
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diff --git a/demo/data_csv.py b/demo/data_csv.py
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--- /dev/null
+++ b/demo/data_csv.py
@@ -0,0 +1,139 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+data = pd.read_excel('dataset/1.xlsx')
+file = np.array(data)
+
+am_1, am_2, am_3, am_4 = 0, 0, 0, 0
+em_1, em_2, em_3, em_4 = 0, 0, 0, 0
+ja_1, ja_2, ja_3, ja_4 = 0, 0, 0, 0
+ap_1, ap_2, ap_3, ap_4 = 0, 0, 0, 0
+
+for item in file:
+    it_area = item[21]
+    if it_area == 'Americas':
+        it_quarter = item[7]
+        if it_quarter == 1:
+            am_1 += item[4]
+        elif it_quarter == 2:
+            am_2 += item[4]
+        elif it_quarter == 3:
+            am_3 += item[4]
+        elif it_quarter == 4:
+            am_4 += item[4]
+    elif it_area == 'EMEA':
+        it_quarter = item[7]
+        if it_quarter == 1:
+            em_1 += item[4]
+        elif it_quarter == 2:
+            em_2 += item[4]
+        elif it_quarter == 3:
+            em_3 += item[4]
+        elif it_quarter == 4:
+            em_4 += item[4]
+    elif it_area == 'APAC':
+        it_quarter = item[7]
+        if it_quarter == 1:
+            ap_1 += item[4]
+        elif it_quarter == 2:
+            ap_2 += item[4]
+        elif it_quarter == 3:
+            ap_3 += item[4]
+        elif it_quarter == 4:
+            ap_4 += item[4]
+        # data_ap.append(item)
+    elif it_area == 'Japan':
+        it_quarter = item[7]
+        if it_quarter == 1:
+            ja_1 += item[4]
+        elif it_quarter == 2:
+            ja_2 += item[4]
+        elif it_quarter == 3:
+            ja_3 += item[4]
+        elif it_quarter == 4:
+            ja_4 += item[4]
+        # data_ja.append(item)
+
+print([am_1, ja_1, ap_1, em_1])
+print([am_2, ja_2, ap_2, em_2])
+print([am_3, ja_3, ap_3, em_3])
+print([am_4, ja_4, ap_4, em_4])
+# color_list = ['y', 'b', 'r', 'g']
+
+# labels = ['Americas', 'Japan', 'APAC', 'EMEA']
+
+# # yellow = [am_1, ja_1, ap_1, em_1]
+# # blue = [am_2, ja_2, ap_2, em_2]
+# # green = [am_3, ja_3, ap_3, em_3]
+# # red = [am_4, ja_4, ap_4, em_4]
+# # err = [1, 1, 1, 1]
+
+# # width = 0.5
+
+# fig, ax = plt.subplots()
+
+# # ax.bar(labels, blue, width, yerr=err, bottom=[yellow, blue, green, red], label='blue')
+# # ax.bar(labels, yellow, width, yerr=err, bottom=[yellow, blue, green, red], label='yellow')
+# # ax.bar(labels, red, width, yerr=err, bottom=[yellow, blue, green, red], label='red')
+# # ax.bar(labels, green, width, yerr=err, bottom=[yellow, blue, green, red], label='green')
+# # ax.set_ylabel('Territory')
+# # ax.set_xlabel('Sales')
+# # ax.legend()  #显示图中左上角的标识区域
+
+# # plt.show()
+
+# # import numpy as np
+# # import matplotlib.pyplot as plt
+# # Emp_data= np.loadtxt('Employedpopulation.csv',delimiter = ",",
+# #                      usecols=(1,2,3,4,5,6,7,8,9,10),dtype=int)
+
+# # # 设置matplotlib正常显示中文和负号
+# # plt.rcParams['font.sans-serif']=['SimHei']   # 用黑体显示中文
+# # plt.rcParams['axes.unicode_minus']=False     # 正常显示负号
+
+# #创建一个绘图对象, 并设置对象的宽度和高度
+# plt.figure(figsize=(12, 4))
+# #绘制全部就业人员柱状图
+# plt.bar(am_1, am_4, width = 0.3, color = 'red')
+# #绘制城镇就业人员柱状图
+# plt.bar(ja_1, ja_4,width = 0.3, color = 'green')
+# #绘制乡村就业人员柱状图
+# plt.bar(ap_1, ap_4, width = 0.3, color = 'blue')
+# plt.bar(em_1, em_4, width = 0.3, color = 'blue')
+
+# # x = [i for i in labels]
+# plt.xlabel('Sales')
+# plt.ylabel('Territory')
+# plt.ylim((30000,80000))
+# # plt.xticks(x)
+# # plt.title("2007-2016年城镇、乡村和全部就业人员情况柱状图")
+# #添加图例
+# plt.legend(('1','2','3','4'))
+# plt.savefig('1.png')
+# plt.show()
+
+
+
+x = ['Americas', 'Japan', 'APAC', 'EMEA']
+y1 = [am_1, ja_1, ap_1, em_1]
+y2 = [am_2, ja_2, ap_2, em_2]
+y3 = [am_3, ja_3, ap_3, em_3]
+y4 = [am_4, ja_4, ap_4, em_4]
+
+plt.bar(x, y4, label="Q1", color='red')
+plt.bar(x, y2, label="Q2",color='orange')
+plt.bar(x, y3, label="Q3", color='lightgreen')
+plt.bar(x, y1, label="Q4", color='yellow')
+
+plt.xticks(np.arange(len(x)), x, rotation=0, fontsize=10)  # 数量多可以采用270度，数量少可以采用340度，得到更好的视图
+plt.legend(loc="upper left")  # 防止label和图像重合显示不出来
+# plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
+plt.ylabel('Sales')
+plt.xlabel('Territory')
+plt.rcParams['savefig.dpi'] = 1600  # 图片像素
+plt.rcParams['figure.dpi'] = 1600  # 分辨率
+plt.rcParams['figure.figsize'] = (25.0, 15.0)  # 尺寸
+plt.title("title")
+plt.savefig('result.png')
+plt.show()
\ No newline at end of file
diff --git a/demo/face.py b/demo/face.py
new file mode 100644
index 0000000..1d719d0
--- /dev/null
+++ b/demo/face.py
@@ -0,0 +1,163 @@
+import os
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import pickle
+import sklearn
+import seaborn
+ 
+from skimage.feature import hog
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.cluster import KMeans
+from scipy.spatial.distance import cdist
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from skimage import io
+from PIL import Image
+from sklearn.naive_bayes import GaussianNB
+ 
+ 
+def extract_hog_features(X):
+    image_descriptors = []
+    for i in range(len(X)):                                         # 此处的X为之前训练部分所有图像的矩阵形式拼接而来，
+        # print(i)                                                  # 所以len(X)实为X中矩阵的个数，即训练部分图像的个数
+        fd, _ = hog(X[i], orientations=9, pixels_per_cell=(16, 16), cells_per_block=(16, 16),
+                            block_norm='L2-Hys', visualize=True)    # 此处的参数细节详见其他文章
+        image_descriptors.append(fd)                                # 拼接得到所有图像的hog特征
+    return image_descriptors                                        # 返回的是训练部分所有图像的hog特征
+
+
+def extract_hog_features_single(X):
+    image_descriptors_single = []
+    fd, _ = hog(X, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(16, 16),
+                            block_norm='L2-Hys', visualize=True)
+    image_descriptors_single.append(fd)
+    return image_descriptors_single
+
+ 
+def read_data(label2id):
+    X = []
+    Y = []
+    path =r'/Users/jiayanhao/OpenBaseLab-Edu/dataset/JAFFE/training_set'
+    for label in os.listdir(path):
+        for img_file in os.listdir(os.path.join(path, label)):              # 遍历
+            image = cv2.imread(os.path.join(path, label, img_file))         # 读取图像
+            result = image/255.0                                            # 图像归一化
+            res = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)                   # 转灰度
+            # cv2.waitKey(0)
+            cv2.destroyAllWindows()
+            X.append(res)                                                   # 将读取到的所有图像的矩阵形式拼接在一起
+            Y.append(label2id[label])                                       # 将读取到的所有图像的标签拼接在一起
+    return X, Y                                                             # 返回的X,Y分别是图像的矩阵表达和图像的标签
+
+ 
+'''
+#svm算法
+#svm = sklearn.svm.SVC(C = 10, kernel='linear') # acc = 0.9
+svm =sklearn.svm.SVC(C=2, kernel='rbf', gamma=10, decision_function_shape='ovr')      #acc=0.9534
+svm.fit(X_train, Y_train)
+Y_predict = svm.predict(X_test)
+'''
+ 
+'''
+#knn算法
+knn = KNeighborsClassifier(n_neighbors=1)     #0.93
+knn.fit(X_train,Y_train)
+print(knn)
+print('测试数据集得分：{:.2f}'.format(knn.score(X_test,Y_test)))
+Y_predict = knn.predict(X_test)
+'''
+ 
+'''
+#决策树算法   0.3+
+tree_D = DecisionTreeClassifier()
+tree_D.fit(X_train, Y_train)
+Y_predict = tree_D.predict(X_test)
+'''
+'''
+#朴素贝叶斯分类   0.67+
+mlt=GaussianNB()
+mlt.fit(X_train, Y_train)
+Y_predict = mlt.predict(X_test)
+'''
+'''
+#逻辑回归分类  0.488
+logistic = LogisticRegression()
+logistic.fit(X_train, Y_train)
+Y_predict = logistic.predict(X_test)
+'''
+
+
+def train_and_infer():
+    label2id = {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}
+    X, Y = read_data(label2id)
+    X_features = extract_hog_features(X)
+    X_train, X_test, Y_train, Y_test = train_test_split(X_features, Y, test_size=0.25, random_state=42)
+
+    Forest = RandomForestClassifier(n_estimators=180,random_state=0)
+    Forest.fit(X_train, Y_train)
+    Y_predict = Forest.predict(X_test)
+    
+    acc = accuracy_score(Y_test, Y_predict)
+    precision = precision_score(Y_test, Y_predict, average='macro')
+    recall = recall_score(Y_test, Y_predict, average='macro')
+    cm = confusion_matrix(Y_test, Y_predict)
+    print(cm)
+    print('Acc: ', acc)
+    print('Precision: ', precision)
+    print('Recall: ', recall)
+    
+    xtick = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+    ytick = xtick
+    
+    f, ax = plt.subplots(figsize=(7, 5))
+    ax.tick_params(axis='y', labelsize=15)
+    ax.tick_params(axis='x', labelsize=15)
+    
+    seaborn.set(font_scale=1.2)
+    plt.rc('font',family='Times New Roman', size=15)
+    
+    seaborn.heatmap(cm,fmt='g', cmap='Blues', annot=True, cbar=True,xticklabels=xtick, yticklabels=ytick, ax=ax)
+    
+    plt.title('Confusion Matrix', fontsize='x-large')
+    
+    plt.show()
+
+
+    # 下面为同一文件夹下多张图片的表情识别
+    # labelid2 = {0:'angry',1: 'disgust',2: 'fear',3:'happy',4:'neutral',5:'sad',6:'surprise'}
+    path=r'/Users/jiayanhao/OpenBaseLab-Edu/dataset/JAFFE/testing_set'
+    i = 1
+    for dir in os.listdir((path)):
+        temp_path = os.path.join(path, dir)
+        for image_file in os.listdir(temp_path):
+            image = cv2.imread(os.path.join(temp_path,image_file))
+            # result = image/255.0
+            # cv2.waitKey(1000)
+            # cv2.destroyAllWindows()
+            result = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            X_Single = extract_hog_features_single(result)
+            predict = Forest.predict(X_Single)         # 可以在这里选择分类器的类别
+            print(i)
+            i += 1
+            if predict == 0:
+                print('angry')
+            elif predict == 1:
+                print('disgust')
+            elif predict == 2:
+                print('fear')
+            elif predict == 3:
+                print('happy')
+            elif predict == 4:
+                print('neutral')
+            elif predict == 5:
+                print('sad')
+            elif predict == 6:
+                print('surprise')
+
+
+if __name__ == '__main__':
+    train_and_infer()
diff --git a/demo/face_with_BaseML.py b/demo/face_with_BaseML.py
new file mode 100644
index 0000000..4b8acee
--- /dev/null
+++ b/demo/face_with_BaseML.py
@@ -0,0 +1,29 @@
+import os
+from re import X
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import pickle
+import sklearn
+import seaborn
+from skimage.feature import hog
+from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
+from scipy.spatial.distance import cdist
+from sklearn.model_selection import train_test_split
+from BaseML import Classification, Regression
+
+def Face():
+    label2id = {'angry': 0, 'disgust': 1, 'fear': 2, 'happy': 3, 'neutral': 4, 'sad': 5, 'surprise': 6}
+    data_path =r'../dataset/JAFFE/training_set'
+    test_path =r'../dataset/JAFFE/testing_set'
+
+    Forest = Classification.cls('RandomForest', n_estimators=180)
+    Forest.read_data_and_pre(label2id, path=data_path)
+    Forest.train()
+    Forest.save()
+    Forest.inference()
+    Forest.plot()
+    Forest.test()
+
+if __name__ == '__main__':
+    Face()
\ No newline at end of file
diff --git a/demo/kmeans.py b/demo/kmeans.py
index 94f3d45..f781498 100644
--- a/demo/kmeans.py
+++ b/demo/kmeans.py
@@ -2,7 +2,7 @@
 import numpy as np
 
 N_CLUSTERS = 7                                     # 类簇的数量
-DATA_PATH = 'Downloads/China_cities.csv'              # 数据集路径
+DATA_PATH = 'dataset/China_cities.csv'              # 数据集路径
 
 
 def city():