added initial rotation augmentation data

cars/data-aug-investigations/all-5/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/all-5/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/all-5/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 607
+base_lr: 0.0099999998
+display: 40
+max_iter: 18210
+lr_policy: "exp"
+gamma: 0.99988908
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 607
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/all-5/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-212033-af57/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/flipped/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/flipped/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/flipped/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 203
+base_lr: 0.0099999998
+display: 25
+max_iter: 6090
+lr_policy: "exp"
+gamma: 0.9996683
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 203
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/flipped/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-112604-e065/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-112604-e065/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-112604-e065/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-112604-e065/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/rot-1/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/rot-1/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/rot-1/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 203
+base_lr: 0.0099999998
+display: 25
+max_iter: 6090
+lr_policy: "exp"
+gamma: 0.9996683
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 203
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/rot-1/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-154441-fb73/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-154441-fb73/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-154441-fb73/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-154441-fb73/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/rot-10/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/rot-10/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/rot-10/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 203
+base_lr: 0.0099999998
+display: 25
+max_iter: 6090
+lr_policy: "exp"
+gamma: 0.9996683
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 203
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/rot-10/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113022-f27a/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113022-f27a/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113022-f27a/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113022-f27a/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/rot-20/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/rot-20/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/rot-20/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 203
+base_lr: 0.0099999998
+display: 25
+max_iter: 6090
+lr_policy: "exp"
+gamma: 0.9996683
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 203
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/rot-20/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113904-3026/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113904-3026/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113904-3026/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113904-3026/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/rot-30/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/rot-30/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/rot-30/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 203
+base_lr: 0.0099999998
+display: 25
+max_iter: 6090
+lr_policy: "exp"
+gamma: 0.9996683
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 203
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/rot-30/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-114443-15ba/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-114443-15ba/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-114443-15ba/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-114443-15ba/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/rot-40/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/rot-40/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/rot-40/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 203
+base_lr: 0.0099999998
+display: 25
+max_iter: 6090
+lr_policy: "exp"
+gamma: 0.9996683
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 203
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/rot-40/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-132146-0bfd/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-132146-0bfd/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-132146-0bfd/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-132146-0bfd/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/rot-5-256batch/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/rot-5-256batch/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/rot-5-256batch/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 7
+test_interval: 102
+base_lr: 0.0099999998
+display: 12
+max_iter: 3060
+lr_policy: "exp"
+gamma: 0.99934
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 102
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/rot-5-256batch/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/train_db"
+    batch_size: 256
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/val_db"
+    batch_size: 256
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/rot-5/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/rot-5/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/rot-5/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 203
+base_lr: 0.0099999998
+display: 25
+max_iter: 6090
+lr_policy: "exp"
+gamma: 0.9996683
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 203
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/rot-5/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

cars/data-aug-investigations/standard/deploy.prototxt

@@ -0,0 +1,341 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 227
+  dim: 227
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+}

cars/data-aug-investigations/standard/original.prototxt

@@ -0,0 +1,388 @@
+name: "AlexNet"
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "train"
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 128
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    stage: "val"
+  }
+  transform_param {
+    crop_size: 227
+  }
+  data_param {
+    batch_size: 32
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    stage: "val"
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+  exclude {
+    stage: "deploy"
+  }
+}
+layer {
+  name: "softmax"
+  type: "Softmax"
+  bottom: "fc8"
+  top: "softmax"
+  include {
+    stage: "deploy"
+  }
+}

cars/data-aug-investigations/standard/solver.prototxt

@@ -0,0 +1,14 @@
+test_iter: 51
+test_interval: 102
+base_lr: 0.0099999998
+display: 12
+max_iter: 3060
+lr_policy: "exp"
+gamma: 0.99934
+momentum: 0.89999998
+weight_decay: 9.9999997e-05
+snapshot: 102
+snapshot_prefix: "snapshot"
+solver_mode: GPU
+net: "train_val.prototxt"
+solver_type: SGD

cars/data-aug-investigations/standard/train_val.prototxt

@@ -0,0 +1,382 @@
+layer {
+  name: "train-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/train_db"
+    batch_size: 128
+    backend: LMDB
+  }
+}
+layer {
+  name: "val-data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/mean.binaryproto"
+  }
+  data_param {
+    source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/val_db"
+    batch_size: 32
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "norm1"
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "norm2"
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 9.9999997e-05
+    beta: 0.75
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+layer {
+  name: "conv5"
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu5"
+  type: "ReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "fc6"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu6"
+  type: "ReLU"
+  bottom: "fc6"
+  top: "fc6"
+}
+layer {
+  name: "drop6"
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc7"
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.0049999999
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layer {
+  name: "relu7"
+  type: "ReLU"
+  bottom: "fc7"
+  top: "fc7"
+}
+layer {
+  name: "drop7"
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "fc8"
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 196
+    weight_filler {
+      type: "gaussian"
+      std: 0.0099999998
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}

report/report.lyx

@@ -21,7 +21,7 @@ minimalistic
 todonotes
 \end_modules
 \maintain_unincluded_children false
-\language english
+\language british
 \language_package default
 \inputencoding auto
 \fontencoding global
@@ -97,7 +97,7 @@ todonotes
 \defskip medskip
 \is_math_indent 0
 \math_numbering_side default
-\quotes_style english
+\quotes_style british
 \dynamic_quotes 0
 \papercolumns 1
 \papersides 1
@@ -295,7 +295,7 @@ More context
 \begin_layout Standard
 Although CNNs can appear opaque when attempting to understand how decisions
  are made, they are not black boxes and there are many ways to affect a
- model's perfomance.
+ model's performance.
  This work presents investigations into how a CNN's performance is affected
  by the subject dataset, the architecture of the network and the parameters
  used when training.