diff --git a/solver.prototxt b/solver.prototxt
new file mode 100644
index 0000000..fdc9015
--- /dev/null
+++ b/solver.prototxt
@@ -0,0 +1,17 @@
+net: "train_val.prototxt"
+base_lr: 0.001
+lr_policy: "step"
+stepsize: 10000
+gamma: 0.100000
+max_iter: 15000
+iter_size: 1
+clip_gradients: 10.000000
+test_iter: 1
+test_interval: 300000
+snapshot: 50
+momentum: 0.900000
+weight_decay: 0.0005
+display: 100
+solver_mode: GPU
+test_initialization: false
+snapshot_prefix: "snapshots/snapshot"
diff --git a/train.py b/train.py
index 53758fb..4b4981d 100644
--- a/train.py
+++ b/train.py
@@ -11,7 +11,6 @@ import matplotlib.pyplot as plt
 import caffe
 import cv2
 import random
-import scipy.ndimage as nd
 
 
 
@@ -285,7 +284,7 @@ def get_args():
 	parser.add_argument("--debug-dir", default='debug', type=str, 
 				help="Dump images for debugging")
 	parser.add_argument("--print-count", default=10, type=int, 
-				help="Dump images for debugging")
+				help="How often to print progress")
 
 	args = parser.parse_args()
 	print args
diff --git a/train_val.prototxt b/train_val.prototxt
new file mode 100644
index 0000000..760af37
--- /dev/null
+++ b/train_val.prototxt
@@ -0,0 +1,845 @@
+input: "data"
+input_dim: 1
+
+# change to input_dim: 1 if not using color inputs
+input_dim: 3
+
+input_dim: 256
+input_dim: 256
+
+input: "gt"
+input_dim: 1
+input_dim: 1
+input_dim: 256
+input_dim: 256
+
+layer {
+  name: "Convolution1"
+  type: "Convolution"
+  bottom: "data"
+  top: "Convolution1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_0_0"
+  type: "ReLU"
+  bottom: "Convolution1"
+  top: "Convolution1"
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "Convolution1"
+  top: "Convolution2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_0_1"
+  type: "ReLU"
+  bottom: "Convolution2"
+  top: "Convolution2"
+}
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "Convolution2"
+  top: "Convolution3"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_0_2"
+  type: "ReLU"
+  bottom: "Convolution3"
+  top: "Convolution3"
+}
+layer {
+  name: "Convolution4"
+  type: "Convolution"
+  bottom: "Convolution3"
+  top: "Convolution4"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_0_3"
+  type: "ReLU"
+  bottom: "Convolution4"
+  top: "Convolution4"
+}
+layer {
+  name: "Convolution5"
+  type: "Convolution"
+  bottom: "Convolution4"
+  top: "Convolution5"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_0_4"
+  type: "ReLU"
+  bottom: "Convolution5"
+  top: "Convolution5"
+}
+layer {
+  name: "Convolution6"
+  type: "Convolution"
+  bottom: "Convolution5"
+  top: "Convolution6"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_0_5"
+  type: "ReLU"
+  bottom: "Convolution6"
+  top: "Convolution6"
+}
+layer {
+  name: "Convolution7"
+  type: "Convolution"
+  bottom: "Convolution6"
+  top: "Convolution7"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_0_6"
+  type: "ReLU"
+  bottom: "Convolution7"
+  top: "Convolution7"
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Convolution1"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "Convolution8"
+  type: "Convolution"
+  bottom: "Pooling1"
+  top: "Convolution8"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_1_0"
+  type: "ReLU"
+  bottom: "Convolution8"
+  top: "Convolution8"
+}
+layer {
+  name: "Convolution9"
+  type: "Convolution"
+  bottom: "Convolution8"
+  top: "Convolution9"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_1_1"
+  type: "ReLU"
+  bottom: "Convolution9"
+  top: "Convolution9"
+}
+layer {
+  name: "Convolution10"
+  type: "Convolution"
+  bottom: "Convolution9"
+  top: "Convolution10"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_1_2"
+  type: "ReLU"
+  bottom: "Convolution10"
+  top: "Convolution10"
+}
+layer {
+  name: "Convolution11"
+  type: "Convolution"
+  bottom: "Convolution10"
+  top: "Convolution11"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_1_3"
+  type: "ReLU"
+  bottom: "Convolution11"
+  top: "Convolution11"
+}
+layer {
+  name: "Convolution12"
+  type: "Convolution"
+  bottom: "Convolution11"
+  top: "Convolution12"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_1_4"
+  type: "ReLU"
+  bottom: "Convolution12"
+  top: "Convolution12"
+}
+layer {
+  name: "Convolution13"
+  type: "Convolution"
+  bottom: "Convolution12"
+  top: "Convolution13"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_1_5"
+  type: "ReLU"
+  bottom: "Convolution13"
+  top: "Convolution13"
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution8"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "Convolution14"
+  type: "Convolution"
+  bottom: "Pooling2"
+  top: "Convolution14"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_2_0"
+  type: "ReLU"
+  bottom: "Convolution14"
+  top: "Convolution14"
+}
+layer {
+  name: "Convolution15"
+  type: "Convolution"
+  bottom: "Convolution14"
+  top: "Convolution15"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_2_1"
+  type: "ReLU"
+  bottom: "Convolution15"
+  top: "Convolution15"
+}
+layer {
+  name: "Convolution16"
+  type: "Convolution"
+  bottom: "Convolution15"
+  top: "Convolution16"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_2_2"
+  type: "ReLU"
+  bottom: "Convolution16"
+  top: "Convolution16"
+}
+layer {
+  name: "Convolution17"
+  type: "Convolution"
+  bottom: "Convolution16"
+  top: "Convolution17"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_2_3"
+  type: "ReLU"
+  bottom: "Convolution17"
+  top: "Convolution17"
+}
+layer {
+  name: "Convolution18"
+  type: "Convolution"
+  bottom: "Convolution17"
+  top: "Convolution18"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_2_4"
+  type: "ReLU"
+  bottom: "Convolution18"
+  top: "Convolution18"
+}
+layer {
+  name: "Pooling3"
+  type: "Pooling"
+  bottom: "Convolution14"
+  top: "Pooling3"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "Convolution19"
+  type: "Convolution"
+  bottom: "Pooling3"
+  top: "Convolution19"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_3_0"
+  type: "ReLU"
+  bottom: "Convolution19"
+  top: "Convolution19"
+}
+layer {
+  name: "Convolution20"
+  type: "Convolution"
+  bottom: "Convolution19"
+  top: "Convolution20"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_3_1"
+  type: "ReLU"
+  bottom: "Convolution20"
+  top: "Convolution20"
+}
+layer {
+  name: "Convolution21"
+  type: "Convolution"
+  bottom: "Convolution20"
+  top: "Convolution21"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_3_2"
+  type: "ReLU"
+  bottom: "Convolution21"
+  top: "Convolution21"
+}
+layer {
+  name: "Convolution22"
+  type: "Convolution"
+  bottom: "Convolution21"
+  top: "Convolution22"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "conv_3_3"
+  type: "ReLU"
+  bottom: "Convolution22"
+  top: "Convolution22"
+}
+layer {
+  name: "upsample_1"
+  type: "Deconvolution"
+  bottom: "Convolution13"
+  top: "upsample_1"
+  param {
+    lr_mult: 0
+	decay_mult: 0
+  }
+  convolution_param {
+    kernel_size: 4
+	num_output: 32
+	group: 32
+	stride: 2
+	pad: 1
+	weight_filler: { type: 'bilinear' }
+	bias_term: false
+  }
+}
+layer {
+  name: "upsample_2"
+  type: "Deconvolution"
+  bottom: "Convolution18"
+  top: "upsample_2"
+  param {
+    lr_mult: 0
+	decay_mult: 0
+  }
+  convolution_param {
+    kernel_size: 8
+	num_output: 32
+	group: 32
+	stride: 4
+	pad: 2
+	weight_filler: { type: 'bilinear' }
+	bias_term: false
+  }
+}
+layer {
+  name: "upsample_3"
+  type: "Deconvolution"
+  bottom: "Convolution22"
+  top: "upsample_3"
+  param {
+    lr_mult: 0
+	decay_mult: 0
+  }
+  convolution_param {
+    kernel_size: 16
+	num_output: 32
+	group: 32
+	stride: 8
+	pad: 4
+	weight_filler: { type: 'bilinear' }
+	bias_term: false
+  }
+}
+layer {
+  name: "merged"
+  type: "Concat"
+  bottom: "Convolution7"
+  bottom: "upsample_1"
+  bottom: "upsample_2"
+  bottom: "upsample_3"
+  top: "merged"
+}
+layer {
+  name: "Convolution26"
+  type: "Convolution"
+  bottom: "merged"
+  top: "Convolution26"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "merged_conv"
+  type: "ReLU"
+  bottom: "Convolution26"
+  top: "Convolution26"
+}
+layer {
+  name: "Convolution27"
+  type: "Convolution"
+  bottom: "Convolution26"
+  top: "Convolution27"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "Convolution27"
+  top: "Convolution27"
+}
+layer {
+  name: "Convolution28"
+  type: "Convolution"
+  bottom: "Convolution27"
+  top: "Convolution28"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "rep_layer_0"
+  type: "ReLU"
+  bottom: "Convolution28"
+  top: "Convolution28"
+}
+layer {
+  name: "Convolution29"
+  type: "Convolution"
+  bottom: "Convolution28"
+  top: "Convolution29"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "Convolution29"
+  top: "Convolution29"
+}
+layer {
+  name: "Convolution30"
+  type: "Convolution"
+  bottom: "Convolution29"
+  top: "out"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 2
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+  }
+}
+layer {
+  name: "baselines_7_loss_0"
+  type: "SoftmaxWithLoss"
+  bottom: "out"
+  bottom: "gt"
+  top: "loss"
+  loss_weight: 1.0
+}