I have build an image segmentation model(Segnet) for which my iou shows decent value but when I predict the test image, I always get dark image. Below is my model, it will be great if you can point problem in my network.
from keras.models import Model, Sequential
from keras.layers import Activation, Dense, BatchNormalization, Dropout, Conv2D, Conv2DTranspose, MaxPooling2D, UpSampling2D, Input, Reshape
from keras.optimizers import Adam, SGD
# Encoding layer
img_input = Input(shape= (256, 256, 3))
x = Conv2D(64, (3, 3), padding='same', name='conv1',strides= (1,1))(img_input)
x = BatchNormalization(name='bn1')(x)
x = Activation('relu')(x)
x = Conv2D(64, (3, 3), padding='same', name='conv2')(x)
x = BatchNormalization(name='bn2')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(128, (3, 3), padding='same', name='conv3')(x)
x = BatchNormalization(name='bn3')(x)
x = Activation('relu')(x)
x = Conv2D(128, (3, 3), padding='same', name='conv4')(x)
x = BatchNormalization(name='bn4')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(256, (3, 3), padding='same', name='conv5')(x)
x = BatchNormalization(name='bn5')(x)
x = Activation('relu')(x)
x = Conv2D(256, (3, 3), padding='same', name='conv6')(x)
x = BatchNormalization(name='bn6')(x)
x = Activation('relu')(x)
x = Conv2D(256, (3, 3), padding='same', name='conv7')(x)
x = BatchNormalization(name='bn7')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(512, (3, 3), padding='same', name='conv8')(x)
x = BatchNormalization(name='bn8')(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='conv9')(x)
x = BatchNormalization(name='bn9')(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='conv10')(x)
x = BatchNormalization(name='bn10')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(512, (3, 3), padding='same', name='conv11')(x)
x = BatchNormalization(name='bn11')(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='conv12')(x)
x = BatchNormalization(name='bn12')(x)
x = Activation('relu')(x)
x = Conv2D(512, (3, 3), padding='same', name='conv13')(x)
x = BatchNormalization(name='bn13')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Dense(1024, activation = 'relu', name='fc1')(x)
x = Dense(1024, activation = 'relu', name='fc2')(x)
# Decoding Layer
x = UpSampling2D()(x)
x = Conv2DTranspose(512, (3, 3), padding='same', name='deconv1')(x)
x = BatchNormalization(name='bn14')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(512, (3, 3), padding='same', name='deconv2')(x)
x = BatchNormalization(name='bn15')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(512, (3, 3), padding='same', name='deconv3')(x)
x = BatchNormalization(name='bn16')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(512, (3, 3), padding='same', name='deconv4')(x)
x = BatchNormalization(name='bn17')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(512, (3, 3), padding='same', name='deconv5')(x)
x = BatchNormalization(name='bn18')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(256, (3, 3), padding='same', name='deconv6')(x)
x = BatchNormalization(name='bn19')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(256, (3, 3), padding='same', name='deconv7')(x)
x = BatchNormalization(name='bn20')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(256, (3, 3), padding='same', name='deconv8')(x)
x = BatchNormalization(name='bn21')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(128, (3, 3), padding='same', name='deconv9')(x)
x = BatchNormalization(name='bn22')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(128, (3, 3), padding='same', name='deconv10')(x)
x = BatchNormalization(name='bn23')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(64, (3, 3), padding='same', name='deconv11')(x)
x = BatchNormalization(name='bn24')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(64, (3, 3), padding='same', name='deconv12')(x)
x = BatchNormalization(name='bn25')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(1, (3, 3), padding='same', name='deconv13')(x)
x = BatchNormalization(name='bn26')(x)
x = Activation('sigmoid')(x)
pred = Reshape((256,256))(x)
below is the code which I use to compile, fit and predict
model = Model(inputs=img_input, outputs=pred)
model.compile(optimizer='adadelta', loss= ["binary_crossentropy"] , metrics=[iou, dice_coef, precision, recall, accuracy])
model.summary()
hist = model.fit(train_dataset, epochs= epochs_num,
validation_data=valid_dataset,
steps_per_epoch=train_steps,
validation_steps=valid_steps,
batch_size= batch_size, shuffle=False, verbose=1)
#prediction
img_1 = cv2.imread(filelist_testx[61], cv2.IMREAD_COLOR)
img_pred = model_1.predict(img_1.reshape(1,256,256,3))
plt.imshow(img_pred.reshape(256, 256), plt.cm.binary_r)
plt.title('Predicted Output')
plt.show()
I am quite confused why always predicted image is black as my IOU is .82.
