セマンティックセグメンテーションの精度

U-netを使って、セマンティックセグメンテーションをやっているのですが、
なかなか安定しないようで、添付グラフのようにギザギザな感じになってしまっています。
このノイズを取り除くには画像を増やす、画像を変えるなど、する以外の方法が何かありますでしょうか？
教えていただければと思います。

画像枚数は127枚を拡張で増やしつつ、学習している感じです。
何かアドバイスなどありましたらよろしくお願いいたします。。
画像は空中写真を使っています。

python
1def image_augmentation(imgs, masks, batch_size, seed):
2    #  create two instances with the same arguments
3    # create dictionary with the input augmentation values
4    data_gen_args = dict(featurewise_center=False,
5                         featurewise_std_normalization=False,
6                         rotation_range=25.,
7                         width_shift_range=0.5,
8                         height_shift_range=0.5,
9                         zoom_range=[0.5, 1],
10                         horizontal_flip=False,
11                         vertical_flip=False,
12                         fill_mode="constant")
13
14    ## use this method with both images and masks
15    image_datagen = ImageDataGenerator(**data_gen_args)
16    mask_datagen = ImageDataGenerator(**data_gen_args)
17
18    ## fit the augmentation model to the images and masks with the same seed
19    image_datagen.fit(imgs, augment=True, seed=seed)
20    mask_datagen.fit(masks, augment=True, seed=seed)
21
22    ## set the parameters for the data to come from (images)
23    image_generator = image_datagen.flow(imgs,
24                                         shuffle=True,
25                                         batch_size=batch_size,
26                                         save_to_dir=None,
27                                         save_prefix='out_image',
28                                         save_format='jpg',
29                                         seed=seed)
30
31    ## set the parameters for the data to come from (masks)
32    mask_generator = mask_datagen.flow(masks,
33                                       shuffle=True,
34                                       batch_size=batch_size,
35                                       save_to_dir=None,
36                                       save_prefix='out_mask',
37                                       save_format='png',
38                                       seed=seed)
39
40    # combine generators into one which yields image and masks
41    train_generator = zip(image_generator, mask_generator)
42
43    ## return the train generator for input in the CNN
44    return train_generator
45
46import warnings
47warnings.filterwarnings('ignore')
48import cv2
49from sklearn.model_selection import train_test_split
50from tensorflow.keras.preprocessing.image import ImageDataGenerator
51import glob
52import numpy as np
53import matplotlib.pyplot as plt
54from tensorflow.keras.callbacks import LearningRateScheduler , EarlyStopping, ModelCheckpoint, ReduceLROnPlateau,TensorBoard
55from tensorflow.keras import backend as K
56import tensorflow as tf
57
58img_size = 512
59
60input_path=glob.glob("trainingData/left_images/*")
61output_path=glob.glob("trainingData/left_groundTruth/*")
62
63train_x=[]
64train_y=[]
65
66for path in input_path:
67    image=cv2.imread(path)
68    image=cv2.resize(image, (img_size, img_size),interpolation = cv2.INTER_LANCZOS4)
69    image = image / 255.
70    train_x.append(image)
71
72for path in output_path:
73    image=cv2.imread(path,0)
74    image=cv2.resize(image, (img_size, img_size),interpolation = cv2.INTER_LANCZOS4)
75    image = image / 255.
76    train_y.append(image)
77    
78train_x=np.array(train_x)
79train_y=np.array(train_y)
80train_y=np.reshape(train_y,(-1,img_size,img_size,1))
81
82X_train, X_test, Y_train, Y_test = train_test_split(train_x, train_y,train_size=0.8,shuffle=True)
83
84# #hard_swishの定義
85def hard_swish(x):
86    return x * (K.relu(x + 3., max_value = 6.) / 6.)
87
88# #swishの定義
89def swish(x):
90    return x * K.sigmoid(x)
91    
92#　正当なU-netのモデル（mishを導入している）
93#  正則化（L2)を導入して変化するかを試す。
94
95from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, MaxPooling2D, UpSampling2D, Dropout, BatchNormalization, Conv2DTranspose
96from tensorflow.keras.models import Model
97from tensorflow.keras.layers import concatenate
98from tensorflow.keras.layers import PReLU   
99from tensorflow.keras.optimizers import RMSprop, Adam
100from tensorflow.keras import backend as K
101from tensorflow.keras import regularizers
102from tf_rectified_adam import RectifiedAdam
103
104input_img = Input(shape=(img_size, img_size, 3))
105
106enc1 = Conv2D(64, kernel_size=3, strides=1, activation=hard_swish,
107              padding="same")(input_img)
108enc1 = BatchNormalization()(enc1)
109enc1 = Conv2D(64, kernel_size=3, strides=1, activation=hard_swish,
110              padding="same")(enc1)
111enc1 = BatchNormalization()(enc1)
112down1 = MaxPooling2D(pool_size=2, strides=2)(enc1)
113
114enc2 = Conv2D(128, kernel_size=3, strides=1, activation=hard_swish,
115              padding="same")(down1)
116enc2 = BatchNormalization()(enc2)
117enc2 = Conv2D(128, kernel_size=3, strides=1, activation=hard_swish,
118              padding="same")(enc2)
119enc2 = BatchNormalization()(enc2)
120down2 = MaxPooling2D(pool_size=2, strides=2)(enc2)
121
122enc3 = Conv2D(256, kernel_size=3, strides=1, activation=hard_swish,
123              padding="same")(down2)
124enc3 = BatchNormalization()(enc3)
125enc3 = Conv2D(256, kernel_size=3, strides=1, activation=hard_swish,
126              padding="same")(enc3)
127enc3 = BatchNormalization()(enc3)
128down3 = MaxPooling2D(pool_size=2, strides=2)(enc3)
129
130enc4 = Conv2D(512, kernel_size=3, strides=1, activation=hard_swish,
131              padding="same")(down3)
132enc4 = BatchNormalization()(enc4)
133enc4 = Conv2D(512, kernel_size=3, strides=1, activation=hard_swish,
134              padding="same")(enc4)
135enc4 = BatchNormalization()(enc4)
136down4 = MaxPooling2D(pool_size=2, strides=2)(enc4)
137
138enc5 = Conv2D(1024, kernel_size=3, strides=1, activation=hard_swish,
139              padding="same")(down4)
140enc5 = BatchNormalization()(enc5)
141enc5 = Conv2D(1024, kernel_size=3, strides=1, activation=hard_swish,
142              padding="same")(enc5)
143enc5 = BatchNormalization()(enc5)
144
145up4 = UpSampling2D(size=2)(enc5)
146dec4 = concatenate([up4, enc4], axis=-1)
147dec4 = Conv2D(512, kernel_size=3, strides=1, activation=hard_swish,
148              padding="same")(dec4)
149dec4 = BatchNormalization()(dec4)
150dec4 = Conv2D(512, kernel_size=3, strides=1, activation=hard_swish,
151              padding="same")(dec4)
152dec4 = BatchNormalization()(dec4)
153
154up3 = UpSampling2D(size=2)(dec4)
155dec3 = concatenate([up3, enc3], axis=-1)
156dec3 = Conv2D(256, kernel_size=3, strides=1, activation=hard_swish,
157              padding="same")(dec3)
158dec3 = BatchNormalization()(dec3)
159dec3 = Conv2D(256, kernel_size=3, strides=1, activation=hard_swish,
160              padding="same")(dec3)
161dec3 = BatchNormalization()(dec3)
162
163up2 = UpSampling2D(size=2)(dec3)
164dec2 = concatenate([up2, enc2], axis=-1)
165dec2 = Conv2D(128, kernel_size=3, strides=1, activation=hard_swish,
166              padding="same")(dec2)
167dec2 = BatchNormalization()(dec2)
168dec2 = Conv2D(128, kernel_size=3, strides=1, activation=hard_swish,
169              padding="same")(dec2)
170dec2 = BatchNormalization()(dec2)
171
172up1 = UpSampling2D(size=2)(dec2)
173dec1 = concatenate([up1, enc1], axis=-1)
174dec1 = Conv2D(64, kernel_size=3, strides=1, activation=hard_swish,
175              padding="same")(dec1)
176dec1 = BatchNormalization()(dec1)
177dec1 = Conv2D(64, kernel_size=3, strides=1, activation=hard_swish,
178              padding="same")(dec1)
179dec1 = BatchNormalization()(dec1)
180
181deca = Conv2D(1,kernel_size=1,strides=1,activation="sigmoid", padding="same")(dec1)
182
183batch_size = 1
184epoch_num =1000
185seed = 2020
186
187train_generator = image_augmentation(X_train, Y_train,batch_size,seed)
188test_generator = image_augmentation(X_test, Y_test,batch_size,seed)
189
190model=Model(inputs=input_img, outputs=deca)
191
192
193#　評価指標IOUのための関数
194
195def true_positive(y_true, y_pred):
196    return K.sum(K.cast(K.equal(y_true * y_pred, 1), K.floatx()))
197
198def true_negative(y_true, y_pred):
199    return K.sum(K.cast(K.equal(y_true + y_pred, 0), K.floatx()))
200
201def false_positive(y_true, y_pred):
202    return K.sum(K.cast(K.less(y_true, y_pred), K.floatx()))
203
204def false_negative(y_true, y_pred):
205    return K.sum(K.cast(K.greater(y_true, y_pred), K.floatx()))
206
207def IoU(y_true, y_pred):
208    y_pred = K.round(y_pred)
209    return true_positive(y_true, y_pred) / (K.epsilon()+false_negative(y_true, y_pred) + true_positive(y_true, y_pred) + false_positive(y_true, y_pred))
210
211
212# 最良の重みのみを保存する。
213cp = ModelCheckpoint("weight/unet_weights_testmodel.hdf5", monitor="val_loss", verbose=1,save_best_only=True, save_weights_only=True)
214
215
216model.compile(optimizer=RectifiedAdam(lr=1e-3), loss='mse', metrics=[IoU])
217
218history = model.fit_generator(train_generator,
219                    steps_per_epoch = X_train.shape[0]//batch_size,
220                    validation_steps = X_test.shape[0]//batch_size,
221                    epochs = epoch_num,
222                    shuffle = True,
223                    verbose = 1,
224                    validation_data = test_generator,
225                    callbacks = [cp])
226