diff --git a/lib/nets.py b/lib/nets.py
deleted file mode 100644
index b75b519..0000000
--- a/lib/nets.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import torch
-from torch import nn
-
-from lib import layers
-
-
-class BaseASPPNet(nn.Module):
-
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-
-    def __init__(self):
-        super(CascadedASPPNet, self).__init__()
-        self.low_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
-        self.high_band_net = BaseASPPNet(2, 32, ((2, 4), (4, 8), (8, 16)))
-
-        self.bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.full_band_net = BaseASPPNet(16, 32)
-
-        self.out = nn.Sequential(
-            layers.Conv2DBNActiv(32, 16, 3, 1, 1),
-            nn.Conv2d(16, 2, 1, bias=False))
-        self.aux_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.offset = 128
-
-    def __call__(self, x):
-        bandw = x.size()[2] // 2
-        aux = torch.cat([
-            self.low_band_net(x[:, :, :bandw]),
-            self.high_band_net(x[:, :, bandw:])
-        ], dim=2)
-
-        h = torch.cat([x, aux], dim=1)
-        h = self.full_band_net(self.bridge(h))
-
-        h = torch.sigmoid(self.out(h))
-        aux = torch.sigmoid(self.aux_out(aux))
-
-        return h, aux
-
-    def predict(self, x):
-        bandw = x.size()[2] // 2
-        aux = torch.cat([
-            self.low_band_net(x[:, :, :bandw]),
-            self.high_band_net(x[:, :, bandw:])
-        ], dim=2)
-
-        h = torch.cat([x, aux], dim=1)
-        h = self.full_band_net(self.bridge(h))
-
-        h = torch.sigmoid(self.out(h))
-        if self.offset > 0:
-            h = h[:, :, :, self.offset:-self.offset]
-            assert h.size()[3] > 0
-
-        return h