bb detection working

brodyh · Nov 20, 2014 · d771232 · d771232
1 parent 36ceac7
commit d771232
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Showing 7 changed files with 322 additions and 141 deletions.
diff --git a/models/brody/deploy.prototxt b/models/brody/deploy.prototxt
@@ -1,13 +1,15 @@
-# This is not brody's net.
-name: "AlexNet"
+name: "BrodyNet"
 input: "data"
 input_dim: 10
 input_dim: 3
-input_dim: 227
-input_dim: 227
+input_dim: 480
+input_dim: 640
+
 layers {
   name: "conv1"
   type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -16,41 +18,49 @@ layers {
     num_output: 96
     kernel_size: 11
     stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
   }
-  bottom: "data"
-  top: "conv1"
 }
 layers {
   name: "relu1"
   type: RELU
   bottom: "conv1"
   top: "conv1"
 }
-layers {
-  name: "norm1"
-  type: LRN
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-  bottom: "conv1"
-  top: "norm1"
-}
 layers {
   name: "pool1"
   type: POOLING
+  bottom: "conv1"
+  top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm1"
-  top: "pool1"
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
 }
 layers {
   name: "conv2"
   type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -60,41 +70,49 @@ layers {
     pad: 2
     kernel_size: 5
     group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
   }
-  bottom: "pool1"
-  top: "conv2"
 }
 layers {
   name: "relu2"
   type: RELU
   bottom: "conv2"
   top: "conv2"
 }
-layers {
-  name: "norm2"
-  type: LRN
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-  bottom: "conv2"
-  top: "norm2"
-}
 layers {
   name: "pool2"
   type: POOLING
+  bottom: "conv2"
+  top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm2"
-  top: "pool2"
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
 }
 layers {
   name: "conv3"
   type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -103,9 +121,15 @@ layers {
     num_output: 384
     pad: 1
     kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
   }
-  bottom: "pool2"
-  top: "conv3"
 }
 layers {
   name: "relu3"
@@ -116,6 +140,8 @@ layers {
 layers {
   name: "conv4"
   type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -125,9 +151,15 @@ layers {
     pad: 1
     kernel_size: 3
     group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
   }
-  bottom: "conv3"
-  top: "conv4"
 }
 layers {
   name: "relu4"
@@ -138,6 +170,8 @@ layers {
 layers {
   name: "conv5"
   type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -147,9 +181,15 @@ layers {
     pad: 1
     kernel_size: 3
     group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
   }
-  bottom: "conv4"
-  top: "conv5"
 }
 layers {
   name: "relu5"
@@ -160,86 +200,140 @@ layers {
 layers {
   name: "pool5"
   type: POOLING
+  bottom: "conv5"
+  top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "conv5"
-  top: "pool5"
 }
 layers {
-  name: "fc6"
-  type: INNER_PRODUCT
+  name: "fc6-conv"
+  type: CONVOLUTION
+  bottom: "pool5"
+  top: "fc6-conv"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
   weight_decay: 0
-  inner_product_param {
+  convolution_param {
     num_output: 4096
+    kernel_size: 6
+    pad: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
   }
-  bottom: "pool5"
-  top: "fc6"
 }
 layers {
   name: "relu6"
   type: RELU
-  bottom: "fc6"
-  top: "fc6"
+  bottom: "fc6-conv"
+  top: "fc6-conv"
 }
 layers {
   name: "drop6"
   type: DROPOUT
+  bottom: "fc6-conv"
+  top: "fc6-conv"
   dropout_param {
-    dropout_ratio: 0.5
+    dropout_ratio: 0.0
   }
-  bottom: "fc6"
-  top: "fc6"
 }
 layers {
-  name: "fc7"
-  type: INNER_PRODUCT
+  name: "fc7-conv"
+  type: CONVOLUTION
+  bottom: "fc6-conv"
+  top: "fc7-conv"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
   weight_decay: 0
-  inner_product_param {
+  convolution_param {
     num_output: 4096
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
   }
-  bottom: "fc6"
-  top: "fc7"
 }
 layers {
   name: "relu7"
   type: RELU
-  bottom: "fc7"
-  top: "fc7"
+  bottom: "fc7-conv"
+  top: "fc7-conv"
 }
 layers {
   name: "drop7"
   type: DROPOUT
+  bottom: "fc7-conv"
+  top: "fc7-conv"
   dropout_param {
-    dropout_ratio: 0.5
+    dropout_ratio: 0.0
   }
-  bottom: "fc7"
-  top: "fc7"
 }
+
 layers {
-  name: "fc8"
-  type: INNER_PRODUCT
+  name: "bb-output"
+  type: CONVOLUTION
+  bottom: "fc7-conv"
+  top: "bb-output"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
   weight_decay: 0
-  inner_product_param {
-    num_output: 1000
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1.0
+    }
+  }
+}
+
+layers {
+  name: "pixel-conv"
+  type: CONVOLUTION
+  bottom: "fc7-conv"
+  top: "pixel-conv"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1.0
+    }
   }
-  bottom: "fc7"
-  top: "fc8"
 }
+
+# Pixel level logistic prediction.
 layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8"
-  top: "prob"
+  name: "pixel-prob"
+  type: SIGMOID
+  bottom: "pixel-conv"
+  top: "pixel-prob"
 }