diff --git a/core/leras/models/Ternaus.py b/core/leras/models/Ternaus.py
new file mode 100644
index 0000000..ad5ffc3
--- /dev/null
+++ b/core/leras/models/Ternaus.py
@@ -0,0 +1,92 @@
+"""
+using https://github.com/ternaus/TernausNet
+TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
+"""
+
+from core.leras import nn
+tf = nn.tf
+
+class Ternaus(nn.ModelBase):
+    def on_build(self, in_ch, base_ch):
+
+        self.features_0 = nn.Conv2D (in_ch, base_ch, kernel_size=3, padding='SAME')
+        self.features_3 = nn.Conv2D (base_ch, base_ch*2, kernel_size=3, padding='SAME')                
+        self.features_6 = nn.Conv2D (base_ch*2, base_ch*4, kernel_size=3, padding='SAME')
+        self.features_8 = nn.Conv2D (base_ch*4, base_ch*4, kernel_size=3, padding='SAME')
+        self.features_11 = nn.Conv2D (base_ch*4, base_ch*8, kernel_size=3, padding='SAME')
+        self.features_13 = nn.Conv2D (base_ch*8, base_ch*8, kernel_size=3, padding='SAME')
+        self.features_16 = nn.Conv2D (base_ch*8, base_ch*8, kernel_size=3, padding='SAME')
+        self.features_18 = nn.Conv2D (base_ch*8, base_ch*8, kernel_size=3, padding='SAME')
+
+        self.blurpool_0 = nn.BlurPool (filt_size=3) 
+        self.blurpool_3 = nn.BlurPool (filt_size=3)
+        self.blurpool_8 = nn.BlurPool (filt_size=3)                
+        self.blurpool_13 = nn.BlurPool (filt_size=3)
+        self.blurpool_18 = nn.BlurPool (filt_size=3)
+
+        self.conv_center = nn.Conv2D (base_ch*8, base_ch*8, kernel_size=3, padding='SAME')
+
+        self.conv1_up = nn.Conv2DTranspose (base_ch*8, base_ch*4, kernel_size=3, padding='SAME')
+        self.conv1 = nn.Conv2D (base_ch*12, base_ch*8, kernel_size=3, padding='SAME')
+
+        self.conv2_up = nn.Conv2DTranspose (base_ch*8, base_ch*4, kernel_size=3, padding='SAME')
+        self.conv2 = nn.Conv2D (base_ch*12, base_ch*8, kernel_size=3, padding='SAME')
+
+        self.conv3_up = nn.Conv2DTranspose (base_ch*8, base_ch*2, kernel_size=3, padding='SAME')
+        self.conv3 = nn.Conv2D (base_ch*6, base_ch*4, kernel_size=3, padding='SAME')
+
+        self.conv4_up = nn.Conv2DTranspose (base_ch*4, base_ch, kernel_size=3, padding='SAME')
+        self.conv4 = nn.Conv2D (base_ch*3, base_ch*2, kernel_size=3, padding='SAME')
+
+        self.conv5_up = nn.Conv2DTranspose (base_ch*2, base_ch//2, kernel_size=3, padding='SAME')
+        self.conv5 = nn.Conv2D (base_ch//2+base_ch, base_ch, kernel_size=3, padding='SAME')
+
+        self.out_conv = nn.Conv2D (base_ch, 1, kernel_size=3, padding='SAME')
+
+    def forward(self, inp):
+        x, = inp
+
+        x = x0 = tf.nn.relu(self.features_0(x))
+        x = self.blurpool_0(x)
+
+        x = x1 = tf.nn.relu(self.features_3(x))
+        x = self.blurpool_3(x)
+
+        x = tf.nn.relu(self.features_6(x))
+        x = x2 = tf.nn.relu(self.features_8(x))
+        x = self.blurpool_8(x)
+
+        x = tf.nn.relu(self.features_11(x))
+        x = x3 = tf.nn.relu(self.features_13(x))
+        x = self.blurpool_13(x)
+
+        x = tf.nn.relu(self.features_16(x))
+        x = x4 = tf.nn.relu(self.features_18(x))
+        x = self.blurpool_18(x)
+
+        x = self.conv_center(x)
+
+        x = tf.nn.relu(self.conv1_up(x))
+        x = tf.concat( [x,x4], nn.conv2d_ch_axis)
+        x = tf.nn.relu(self.conv1(x))
+
+        x = tf.nn.relu(self.conv2_up(x))
+        x = tf.concat( [x,x3], nn.conv2d_ch_axis)
+        x = tf.nn.relu(self.conv2(x))
+
+        x = tf.nn.relu(self.conv3_up(x))
+        x = tf.concat( [x,x2], nn.conv2d_ch_axis)
+        x = tf.nn.relu(self.conv3(x))
+
+        x = tf.nn.relu(self.conv4_up(x))
+        x = tf.concat( [x,x1], nn.conv2d_ch_axis)
+        x = tf.nn.relu(self.conv4(x))
+
+        x = tf.nn.relu(self.conv5_up(x))
+        x = tf.concat( [x,x0], nn.conv2d_ch_axis)
+        x = tf.nn.relu(self.conv5(x))
+
+        logits = self.out_conv(x)
+        return logits, tf.nn.sigmoid(logits)
+        
+nn.Ternaus = Ternaus
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