diff --git a/models/brody/deploy.prototxt b/models/brody/deploy.prototxt
deleted file mode 100644
index 85e72f10774..00000000000
--- a/models/brody/deploy.prototxt
+++ /dev/null
@@ -1,339 +0,0 @@
-name: "BrodyNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 480
-input_dim: 640
-
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6-conv"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "fc6-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 6
-    pad: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-layers {
-  name: "fc7-conv"
-  type: CONVOLUTION
-  bottom: "fc6-conv"
-  top: "fc7-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-
-layers {
-  name: "bb-output"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "bb-output"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-conv"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "pixel-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-# Pixel level logistic prediction.
-layers {
-  name: "pixel-prob"
-  type: SIGMOID
-  bottom: "pixel-conv"
-  top: "pixel-prob"
-}
diff --git a/models/brody/deploy_deeppy.prototxt b/models/brody/deploy_deeppy.prototxt
deleted file mode 100644
index c3425109a37..00000000000
--- a/models/brody/deploy_deeppy.prototxt
+++ /dev/null
@@ -1,359 +0,0 @@
-name: "BrodyNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 480
-input_dim: 640
-
-layers {
-  name: "L0"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "L0"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "L0"
-  top: "L0"
-}
-layers {
-  name: "norm1"
-  type: LRN_FIXED
-  bottom: "L0"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "norm1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "L1"
-  type: CONVOLUTION
-  bottom: "pool1"
-  top: "L1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "L1"
-  top: "L1"
-}
-layers {
-  name: "norm2"
-  type: LRN_FIXED
-  bottom: "L1"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "norm2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "L2"
-  type: CONVOLUTION
-  bottom: "pool2"
-  top: "L2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "L2"
-  top: "L2"
-}
-layers {
-  name: "L3"
-  type: CONVOLUTION
-  bottom: "L2"
-  top: "L3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "L3"
-  top: "L3"
-}
-layers {
-  name: "L4"
-  type: CONVOLUTION
-  bottom: "L3"
-  top: "L4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "L4"
-  top: "L4"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "L4"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "L5"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "L5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 6
-    pad: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "L5"
-  top: "L5"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT_FIXED
-  bottom: "L5"
-  top: "L5d"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "L6"
-  type: CONVOLUTION
-  bottom: "L5d"
-  top: "L6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "L6"
-  top: "L6"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT_FIXED
-  bottom: "L6"
-  top: "L6d"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-
-layers {
-  name: "L7.regression"
-  type: CONVOLUTION
-  bottom: "L6d"
-  top: "L7.regression"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 320
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "L7"
-  type: CONVOLUTION
-  bottom: "L6d"
-  top: "L7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-tile"
-  type: TILING
-  bottom: "L7"
-  top: "pixel-conv-tiled"
-  tiling_param {
-    tile_dim: 8
-  }
-}
-
-layers {
-  name: "bb-tile"
-  type: TILING
-  bottom: "L7.regression"
-  top: "bb-output-tiled"
-  tiling_param {
-    tile_dim: 8
-  }
-}
-
-# Pixel level softmax prediction.
-layers {
-  name: "pixel-prob"
-  type: SOFTMAX
-  bottom: "pixel-conv-tiled"
-  top: "pixel-prob"
-}
diff --git a/models/brody/deploy_softmax.prototxt b/models/brody/deploy_softmax.prototxt
deleted file mode 100644
index c19d427f280..00000000000
--- a/models/brody/deploy_softmax.prototxt
+++ /dev/null
@@ -1,359 +0,0 @@
-name: "BrodyNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 480
-input_dim: 640
-
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6-conv"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "fc6-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 6
-    pad: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-layers {
-  name: "fc7-conv"
-  type: CONVOLUTION
-  bottom: "fc6-conv"
-  top: "fc7-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-
-layers {
-  name: "bb-output"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "bb-output"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-conv"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "pixel-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-tile"
-  type: TILING
-  bottom: "pixel-conv"
-  top: "pixel-conv-tiled"
-  tiling_param {
-    tile_dim: 4
-  }
-}
-
-layers {
-  name: "bb-tile"
-  type: TILING
-  bottom: "bb-output"
-  top: "bb-output-tiled"
-  tiling_param {
-    tile_dim: 4
-  }
-}
-
-# Pixel level softmax prediction.
-layers {
-  name: "pixel-prob"
-  type: SOFTMAX
-  bottom: "pixel-conv-tiled"
-  top: "pixel-prob"
-}
diff --git a/models/brody/solver.prototxt b/models/brody/solver.prototxt
deleted file mode 100644
index cc7b119e95e..00000000000
--- a/models/brody/solver.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-net: "models/brody/train_val_brody.prototxt"
-test_iter: 20
-test_interval: 5000
-test_compute_loss: true
-base_lr: 0.0000001
-lr_policy: "step"
-gamma: 0.1
-stepsize: 100000
-display: 20
-max_iter: 1450000
-momentum: 0.9
-weight_decay: 0.00005
-snapshot: 10000
-snapshot_prefix: "models/brody/snapshots/test_test"
-solver_mode: GPU
diff --git a/models/brody/solver_driving.prototxt b/models/brody/solver_driving.prototxt
deleted file mode 100644
index 5472b90de0b..00000000000
--- a/models/brody/solver_driving.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-net: "models/brody/train_val_driving.prototxt"
-test_iter: 20
-test_interval: 5000
-test_compute_loss: true
-base_lr: 0.001
-lr_policy: "step"
-gamma: 0.1
-stepsize: 100000
-display: 20
-max_iter: 1450000
-momentum: 0.9
-weight_decay: 0.00005
-snapshot: 10000
-snapshot_prefix: "models/brody/caffe_driving"
-solver_mode: GPU
diff --git a/models/brody/solver_driving_softmax.prototxt b/models/brody/solver_driving_softmax.prototxt
deleted file mode 100644
index 1036a393acd..00000000000
--- a/models/brody/solver_driving_softmax.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-net: "models/brody/train_val_driving_softmax_norm.prototxt"
-test_iter: 20
-test_interval: 5000
-test_compute_loss: true
-base_lr: 0.002
-lr_policy: "step"
-gamma: 0.1
-stepsize: 100000
-display: 20
-max_iter: 1450000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 1000
-snapshot_prefix: "models/brody/driving_softmax_8x8_norm"
-solver_mode: GPU
diff --git a/models/brody/train_val_brody.prototxt b/models/brody/train_val_brody.prototxt
deleted file mode 100644
index 51ba17ef565..00000000000
--- a/models/brody/train_val_brody.prototxt
+++ /dev/null
@@ -1,529 +0,0 @@
-name: "BrodyNet"
-
-# Training input.
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  data_param {
-    source: "driving_img_train"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "driving_img_mean.binaryproto"
-  }
-  include: { phase: TRAIN }
-}
-
-# Bounding box label and pixel label.
-layers {
-  name: "label"
-  type: DATA
-  top: "label"
-  data_param {
-    source: "driving_label_train"
-    backend: LMDB
-    batch_size: 5
-  }
-  include: { phase: TRAIN }
-}
-
-# Test input.
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  data_param {
-    source: "driving_img_test"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "driving_img_mean.binaryproto"
-  }
-  include: { phase: TEST }
-}
-
-# Bounding box label and pixel label.
-layers {
-  name: "label"
-  type: DATA
-  top: "label"
-  data_param {
-    source: "driving_label_test"
-    backend: LMDB
-    batch_size: 5
-  }
-  include: { phase: TEST }
-}
-
-# Split label layer into pixel and bounding box label.
-layers {
-  name: "slice-label"
-  type: SLICE
-  bottom: "label"
-  top: "pixel-label"
-  top: "bb-label"
-  top: "height-label"
-  top: "norm-label"
-  slice_param {
-    slice_dim: 1
-    slice_point: 16
-    slice_point: 80
-    slice_point: 96
-  }
-}
-
-# Concatenate the pixel labels 4 folds such that it can be used to mask
-# all 4 dimensions of the bounding box predictions.
-layers {
-  name: "pixel-block"
-  type: CONCAT
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  top: "pixel-block"
-  concat_param {
-    concat_dim: 1
-  }
-}
-
-layers {
-  name: "height-block"
-  type: CONCAT
-  bottom: "height-label"
-  bottom: "height-label"
-  bottom: "height-label"
-  bottom: "height-label"
-  top: "height-block"
-  concat_param {
-    concat_dim: 1
-  }
-}
-
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6-conv"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "fc6-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 6
-    pad: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-layers {
-  name: "fc7-conv"
-  type: CONVOLUTION
-  bottom: "fc6-conv"
-  top: "fc7-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-
-layers {
-  name: "bb-output"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "bb-output"
-  blobs_lr: 100
-  blobs_lr: 200
-  weight_decay: 0.00001
-  weight_decay: 0
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-conv"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "pixel-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-# Pixel level logistic prediction.
-#layers {
-#  name: "pixel-prob"
-#  type: SIGMOID
-#  bottom: "pixel-conv"
-#  top: "pixel-prob"
-#}
-
-# Pixel level logistic loss.
-layers {
-  name: "pixel-loss"
-  type: SIGMOID_CROSS_ENTROPY_LOSS
-  bottom: "pixel-conv"
-  bottom: "pixel-label"
-  top: "pixel-loss"
-}
-
-# Masking the bounding boxes with input label.
-layers {
-  name: "bb-prob-mask"
-  type: ELTWISE
-  bottom: "pixel-block"
-  bottom: "bb-output"
-  top: "bb-masked-output"
-  eltwise_param {
-    operation: PROD
-  }
-}
-
-layers {
-  name: "bb-loss"
-  type: L1_LOSS
-  bottom: "bb-masked-output"
-  bottom: "bb-label"
-  top: "bb-loss"
-  loss_weight: 1
-}
-
-# Squared loss on the bounding boxes.
-#layers {
-#  name: "bb-loss"
-#  type: EUCLIDEAN_LOSS
-#  bottom: "bb-masked-output"
-#  bottom: "bb-label"
-#  top: "bb-loss"
-#  loss_weight: 0.01
-#}
-
-# L1 error loss
-#layers {
-#  name: "bb-diff"
-#  type: ELTWISE
-#  bottom: "bb-masked-output"
-#  bottom: "bb-label"
-#  eltwise_param {
-#    operation: SUM
-#    coeff: 1.0
-#    coeff: -1.0
-#  }
-#  top: "bb-diff"
-#}
-
-#layers {
-#  name: "bb-loss"
-#  type: ABSVAL
-#  bottom: "bb-diff"
-#  top: "bb-loss"
-#  # 1 / (20 * 15 * 64)
-#  loss_weight: 0.00000000001
-#}
-
-#layers {
-#  name: "bb-loss-pow2"
-#  type: POWER
-#  bottom: "bb-diff"
-#  top: "bb-loss-pow2"
-#  # 1 / (20 * 15 * 64)
-#  power_param {
-#    power: 2
-#  }
-#}
-
-#layers {
-#  name: "bb-loss-height-normalize"
-#  type: ELTWISE
-#  bottom: "bb-loss-pow2"
-#  bottom: "height-block"
-#  eltwise_param {
-#    operation: PROD
-#  }
-#  top: "bb-loss"
-#  loss_weight: 0.1
-#}
-
-#layers {
-#  name: "bb-loss-silence"
-#  type: SILENCE
-#  bottom: "bb-loss"
-#}
diff --git a/models/brody/train_val_driving.prototxt b/models/brody/train_val_driving.prototxt
deleted file mode 100644
index e1984e2d4ae..00000000000
--- a/models/brody/train_val_driving.prototxt
+++ /dev/null
@@ -1,505 +0,0 @@
-name: "DrivingNet"
-
-# Training input.
-layers {
-  name: "data"
-  type: DRIVING_DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "driving_train"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "driving_mean.binaryproto"
-  }
-  include: { phase: TRAIN }
-}
-
-# Test input.
-layers {
-  name: "data"
-  type: DRIVING_DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "driving_test"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "driving_mean.binaryproto"
-  }
-  include: { phase: TEST }
-}
-
-# Split label layer into pixel and bounding box label.
-layers {
-  name: "slice-label"
-  type: SLICE
-  bottom: "label"
-  top: "pixel-label"
-  top: "bb-label"
-  top: "height-label"
-  top: "norm-label"
-  slice_param {
-    slice_dim: 1
-    slice_point: 16
-    slice_point: 80
-    slice_point: 96
-  }
-}
-
-# Concatenate the pixel labels 4 folds such that it can be used to mask
-# all 4 dimensions of the bounding box predictions.
-layers {
-  name: "pixel-block"
-  type: CONCAT
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  top: "pixel-block"
-  concat_param {
-    concat_dim: 1
-  }
-}
-
-layers {
-  name: "height-block"
-  type: CONCAT
-  bottom: "height-label"
-  bottom: "height-label"
-  bottom: "height-label"
-  bottom: "height-label"
-  top: "height-block"
-  concat_param {
-    concat_dim: 1
-  }
-}
-
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6-conv"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "fc6-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 6
-    pad: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-layers {
-  name: "fc7-conv"
-  type: CONVOLUTION
-  bottom: "fc6-conv"
-  top: "fc7-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-
-layers {
-  name: "bb-output"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "bb-output"
-  blobs_lr: 100
-  blobs_lr: 200
-  weight_decay: 0.00001
-  weight_decay: 0
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-conv"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "pixel-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-# Pixel level logistic prediction.
-#layers {
-#  name: "pixel-prob"
-#  type: SIGMOID
-#  bottom: "pixel-conv"
-#  top: "pixel-prob"
-#}
-
-# Pixel level logistic loss.
-layers {
-  name: "pixel-loss"
-  type: SIGMOID_CROSS_ENTROPY_LOSS
-  bottom: "pixel-conv"
-  bottom: "pixel-label"
-  top: "pixel-loss"
-}
-
-# Masking the bounding boxes with input label.
-layers {
-  name: "bb-prob-mask"
-  type: ELTWISE
-  bottom: "pixel-block"
-  bottom: "bb-output"
-  top: "bb-masked-output"
-  eltwise_param {
-    operation: PROD
-  }
-}
-
-layers {
-  name: "bb-loss"
-  type: L1_LOSS
-  bottom: "bb-masked-output"
-  bottom: "bb-label"
-  top: "bb-loss"
-  loss_weight: 1
-}
-
-# Squared loss on the bounding boxes.
-#layers {
-#  name: "bb-loss"
-#  type: EUCLIDEAN_LOSS
-#  bottom: "bb-masked-output"
-#  bottom: "bb-label"
-#  top: "bb-loss"
-#  loss_weight: 0.01
-#}
-
-# L1 error loss
-#layers {
-#  name: "bb-diff"
-#  type: ELTWISE
-#  bottom: "bb-masked-output"
-#  bottom: "bb-label"
-#  eltwise_param {
-#    operation: SUM
-#    coeff: 1.0
-#    coeff: -1.0
-#  }
-#  top: "bb-diff"
-#}
-
-#layers {
-#  name: "bb-loss"
-#  type: ABSVAL
-#  bottom: "bb-diff"
-#  top: "bb-loss"
-#  # 1 / (20 * 15 * 64)
-#  loss_weight: 0.00000000001
-#}
-
-#layers {
-#  name: "bb-loss-pow2"
-#  type: POWER
-#  bottom: "bb-diff"
-#  top: "bb-loss-pow2"
-#  # 1 / (20 * 15 * 64)
-#  power_param {
-#    power: 2
-#  }
-#}
-
-#layers {
-#  name: "bb-loss-height-normalize"
-#  type: ELTWISE
-#  bottom: "bb-loss-pow2"
-#  bottom: "height-block"
-#  eltwise_param {
-#    operation: PROD
-#  }
-#  top: "bb-loss"
-#  loss_weight: 0.1
-#}
-
-#layers {
-#  name: "bb-loss-silence"
-#  type: SILENCE
-#  bottom: "bb-loss"
-#}
diff --git a/models/brody/train_val_driving_softmax.prototxt b/models/brody/train_val_driving_softmax.prototxt
deleted file mode 100644
index 88938d66113..00000000000
--- a/models/brody/train_val_driving_softmax.prototxt
+++ /dev/null
@@ -1,534 +0,0 @@
-name: "DrivingNet"
-
-# Training input.
-layers {
-  name: "data"
-  type: DRIVING_DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "new_driving_train"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "driving_mean.binaryproto"
-  }
-  include: { phase: TRAIN }
-}
-
-# Test input.
-layers {
-  name: "data"
-  type: DRIVING_DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "new_driving_test"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "driving_mean.binaryproto"
-  }
-  include: { phase: TEST }
-}
-
-# Split label layer into pixel and bounding box label.
-layers {
-  name: "slice-label"
-  type: SLICE
-  bottom: "label"
-  top: "pixel-label"
-  top: "bb-label"
-  top: "height-label"
-  top: "norm-label"
-  slice_param {
-    slice_dim: 1
-    slice_point: 1
-    slice_point: 5
-    slice_point: 6
-  }
-}
-
-# Concatenate the pixel labels 4 folds such that it can be used to mask
-# all 4 dimensions of the bounding box predictions.
-layers {
-  name: "pixel-block"
-  type: CONCAT
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  top: "pixel-block"
-  concat_param {
-    concat_dim: 1
-  }
-}
-
-layers {
-  name: "height-block"
-  type: CONCAT
-  bottom: "height-label"
-  bottom: "height-label"
-  bottom: "height-label"
-  bottom: "height-label"
-  top: "height-block"
-  concat_param {
-    concat_dim: 1
-  }
-}
-
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6-conv"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "fc6-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 6
-    pad: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-layers {
-  name: "fc7-conv"
-  type: CONVOLUTION
-  bottom: "fc6-conv"
-  top: "fc7-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-  dropout_param {
-    dropout_ratio: 0.0
-  }
-}
-
-layers {
-  name: "bb-output"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "bb-output"
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 0.1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-conv"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "pixel-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1.0
-    }
-  }
-}
-
-layers {
-  name: "pixel-tile"
-  type: TILING
-  bottom: "pixel-conv"
-  top: "pixel-conv-tiled"
-  tiling_param {
-    tile_dim: 8
-  }
-}
-
-layers {
-  name: "bb-tile"
-  type: TILING
-  bottom: "bb-output"
-  top: "bb-output-tiled"
-  tiling_param {
-    tile_dim: 8
-  }
-}
-
-# Pixel level logistic prediction.
-#layers {
-#  name: "pixel-prob"
-#  type: SIGMOID
-#  bottom: "pixel-conv"
-#  top: "pixel-prob"
-#}
-
-# Pixel level logistic loss.
-layers {
-  name: "pixel-loss"
-  type: SOFTMAX_LOSS
-  bottom: "pixel-conv-tiled"
-  bottom: "pixel-label"
-  top: "pixel-loss"
-}
-
-# Pixel level logistic loss.
-#layers {
-#  name: "pixel-loss"
-#  type: SIGMOID_CROSS_ENTROPY_LOSS
-#  bottom: "pixel-conv-tiled"
-#  bottom: "pixel-label"
-#  top: "pixel-loss"
-#}
-
-# Masking the bounding boxes with input label.
-layers {
-  name: "bb-prob-mask"
-  type: ELTWISE
-  bottom: "bb-output-tiled"
-  bottom: "pixel-block"
-  top: "bb-masked-output"
-  eltwise_param {
-    operation: PROD
-  }
-}
-
-layers {
-  name: "bb-loss"
-  type: L1_LOSS
-  bottom: "bb-masked-output"
-  bottom: "bb-label"
-  top: "bb-loss"
-  loss_weight: 1
-}
-
-# Squared loss on the bounding boxes.
-#layers {
-#  name: "bb-loss"
-#  type: EUCLIDEAN_LOSS
-#  bottom: "bb-masked-output"
-#  bottom: "bb-label"
-#  top: "bb-loss"
-#  loss_weight: 0.01
-#}
-
-# L1 error loss
-#layers {
-#  name: "bb-diff"
-#  type: ELTWISE
-#  bottom: "bb-masked-output"
-#  bottom: "bb-label"
-#  eltwise_param {
-#    operation: SUM
-#    coeff: 1.0
-#    coeff: -1.0
-#  }
-#  top: "bb-diff"
-#}
-
-#layers {
-#  name: "bb-loss"
-#  type: ABSVAL
-#  bottom: "bb-diff"
-#  top: "bb-loss"
-#  # 1 / (20 * 15 * 64)
-#  loss_weight: 0.00000000001
-#}
-
-#layers {
-#  name: "bb-loss-pow2"
-#  type: POWER
-#  bottom: "bb-diff"
-#  top: "bb-loss-pow2"
-#  # 1 / (20 * 15 * 64)
-#  power_param {
-#    power: 2
-#  }
-#}
-
-#layers {
-#  name: "bb-loss-height-normalize"
-#  type: ELTWISE
-#  bottom: "bb-loss-pow2"
-#  bottom: "height-block"
-#  eltwise_param {
-#    operation: PROD
-#  }
-#  top: "bb-loss"
-#  loss_weight: 0.1
-#}
-
-#layers {
-#  name: "bb-loss-silence"
-#  type: SILENCE
-#  bottom: "bb-loss"
-#}
diff --git a/models/brody/train_val_dummy.prototxt b/models/brody/train_val_dummy.prototxt
deleted file mode 100644
index 695533d1cc9..00000000000
--- a/models/brody/train_val_dummy.prototxt
+++ /dev/null
@@ -1,452 +0,0 @@
-name: "BrodyDummyNet"
-
-# Training input.
-layers {
-  name: "data"
-  type: DUMMY_DATA
-  top: "data"
-  dummy_data_param {
-    num: 25
-    channels: 3
-    height: 480
-    width: 640
-    data_filler {
-      type: "uniform"
-      min: 0
-      max: 128
-    }
-  }
-  include: { phase: TRAIN }
-}
-
-# Test input.
-layers {
-  name: "data"
-  type: DUMMY_DATA
-  top: "data"
-  dummy_data_param {
-    num: 25
-    channels: 3
-    height: 480
-    width: 640
-    data_filler {
-      type: "uniform"
-      min: 0
-      max: 128
-    }
-  }
-  include: { phase: TEST }
-}
-
-# Bounding box output.
-layers {
-  name: "bb-label"
-  type: DUMMY_DATA
-  top: "bb-label"
-  dummy_data_param {
-    num: 25
-    channels: 64
-    height: 15
-    width: 20
-    data_filler {
-      type: "uniform"
-      min: 0
-      max: 640
-    }
-  }
-}
-
-# Pixel label indicating the presence of cars.
-layers {
-  name: "pixel-label"
-  type: DUMMY_DATA
-  top: "pixel-label"
-  dummy_data_param {
-    num: 25
-    channels: 16
-    height: 15
-    width: 20
-    data_filler {
-      type: "uniform"
-      min: 0
-      max: 1
-    }
-  }
-}
-
-# Concatenate the pixel labels 4 folds such that it can be used to mask
-# all 4 dimensions of the bounding box predictions.
-layers {
-  name: "pixel-block"
-  type: CONCAT
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  bottom: "pixel-label"
-  top: "pixel-block"
-  concat_param {
-    concat_dim: 1
-  }
-}
-
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6-conv"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "fc6-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7-conv"
-  type: CONVOLUTION
-  bottom: "fc6-conv"
-  top: "fc7-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-
-layers {
-  name: "bb-output"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "bb-output"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layers {
-  name: "pixel-conv"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "pixel-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-# Pixel level logistic prediction.
-layers {
-  name: "pixel-prob"
-  type: SIGMOID
-  bottom: "pixel-conv"
-  top: "pixel-prob"
-}
-
-# Pixel level logistic loss.
-layers {
-  name: "pixel-loss"
-  type: SIGMOID_CROSS_ENTROPY_LOSS
-  bottom: "pixel-prob"
-  bottom: "pixel-label"
-  top: "pixel-loss"
-}
-
-# Masking the bounding boxes with input label.
-layers {
-  name: "bb-prob-mask"
-  type: ELTWISE
-  bottom: "pixel-block"
-  bottom: "bb-output"
-  top: "bb-final-output"
-  eltwise_param {
-    operation: PROD
-  }
-}
-
-# Squared loss on the bounding boxes.
-layers {
-  name: "bb-loss"
-  type: EUCLIDEAN_LOSS
-  bottom: "bb-final-output"
-  bottom: "bb-label"
-  top: "bb-loss"
-}
diff --git a/models/brody/train_val_old_alexoverfeat.prototxt b/models/brody/train_val_old_alexoverfeat.prototxt
deleted file mode 100644
index b6b5c33eca8..00000000000
--- a/models/brody/train_val_old_alexoverfeat.prototxt
+++ /dev/null
@@ -1,345 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "random_train_lmdb"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "random_image_mean.binaryproto"
-  }
-  include: { phase: TRAIN }
-}
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "random_val_lmdb"
-    backend: LMDB
-    batch_size: 5
-  }
-  transform_param {
-    mean_file: "random_image_mean.binaryproto"
-  }
-  include: { phase: TEST }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6-conv"
-  type: CONVOLUTION
-  bottom: "pool5"
-  top: "fc6-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 6
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6-conv"
-  top: "fc6-conv"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7-conv"
-  type: CONVOLUTION
-  bottom: "fc6-conv"
-  top: "fc7-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 4096
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7-conv"
-  top: "fc7-conv"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8-conv"
-  type: CONVOLUTION
-  bottom: "fc7-conv"
-  top: "fc8-conv"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 1000
-    kernel_size: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "fc8-conv"
-  bottom: "label"
-  top: "accuracy"
-  include: { phase: TEST }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8-conv"
-  bottom: "label"
-  top: "loss"
-}
diff --git a/models/brody/readme.md b/models/drive_net/readme.md
similarity index 100%
rename from models/brody/readme.md
rename to models/drive_net/readme.md
diff --git a/models/brody/solver_normalization.prototxt b/models/drive_net/solver.prototxt
similarity index 100%
rename from models/brody/solver_normalization.prototxt
rename to models/drive_net/solver.prototxt
diff --git a/models/brody/train_val_driving_normalization.prototxt b/models/drive_net/train_val.prototxt
similarity index 100%
rename from models/brody/train_val_driving_normalization.prototxt
rename to models/drive_net/train_val.prototxt