[models] adding zoo readme; caffenet, alexnet, and rcnn models in zoo format

11 files changed, 11 insertions, 1474 deletions

diff --git a/examples/imagenet/alexnet_deploy.prototxt b/examples/imagenet/alexnet_deploy.prototxt
deleted file mode 100644
index d010753f..00000000
--- a/examples/imagenet/alexnet_deploy.prototxt
+++ /dev/null
@@ -1,244 +0,0 @@
-name: "AlexNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-  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
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "norm1"
-  top: "pool1"
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-  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
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "norm2"
-  top: "pool2"
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-  bottom: "pool2"
-  top: "conv3"
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-  bottom: "conv3"
-  top: "conv4"
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-  bottom: "conv4"
-  top: "conv5"
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "conv5"
-  top: "pool5"
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-  }
-  bottom: "pool5"
-  top: "fc6"
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-  }
-  bottom: "fc6"
-  top: "fc7"
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 1000
-  }
-  bottom: "fc7"
-  top: "fc8"
-}
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8"
-  top: "prob"
-}
diff --git a/examples/imagenet/alexnet_solver.prototxt b/examples/imagenet/alexnet_solver.prototxt
deleted file mode 100644
index 94bda7f3..00000000
--- a/examples/imagenet/alexnet_solver.prototxt
+++ /dev/null
@@ -1,14 +0,0 @@
-net: "examples/imagenet/alexnet_train_val.prototxt"
-test_iter: 1000
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 100000
-display: 20
-max_iter: 450000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/imagenet/caffe_alexnet"
-solver_mode: GPU
diff --git a/examples/imagenet/alexnet_train_val.prototxt b/examples/imagenet/alexnet_train_val.prototxt
deleted file mode 100644
index 3fa46773..00000000
--- a/examples/imagenet/alexnet_train_val.prototxt
+++ /dev/null
@@ -1,344 +0,0 @@
-name: "AlexNet"
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_leveldb"
-    batch_size: 256
-  }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
-  }
-  include: { phase: TRAIN }
-}
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_leveldb"
-    batch_size: 50
-  }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
-  include: { phase: TEST }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-  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
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "norm1"
-  top: "pool1"
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-  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
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "norm2"
-  top: "pool2"
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-  bottom: "pool2"
-  top: "conv3"
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-  bottom: "conv3"
-  top: "conv4"
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-  bottom: "conv4"
-  top: "conv5"
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "conv5"
-  top: "pool5"
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-  bottom: "pool5"
-  top: "fc6"
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-  bottom: "fc6"
-  top: "fc7"
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-  bottom: "fc7"
-  top: "fc8"
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "fc8"
-  bottom: "label"
-  top: "accuracy"
-  include: { phase: TEST }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
diff --git a/examples/imagenet/get_caffe_alexnet_model.sh b/examples/imagenet/get_caffe_alexnet_model.sh
deleted file mode 100755
index 7312ed93..00000000
--- a/examples/imagenet/get_caffe_alexnet_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the caffe reference imagenet model
-# for ilsvrc image classification and deep feature extraction
-
-MODEL=caffe_alexnet_model
-CHECKSUM=29eb495b11613825c1900382f5286963
-
-if [ -f $MODEL ]; then
-  echo "Model already exists. Checking md5..."
-  os=`uname -s`
-  if [ "$os" = "Linux" ]; then
-    checksum=`md5sum $MODEL | awk '{ print $1 }'`
-  elif [ "$os" = "Darwin" ]; then
-    checksum=`cat $MODEL | md5`
-  fi
-  if [ "$checksum" = "$CHECKSUM" ]; then
-    echo "Model checksum is correct. No need to download."
-    exit 0
-  else
-    echo "Model checksum is incorrect. Need to download again."
-  fi
-fi
-
-echo "Downloading..."
-
-wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
-
-echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
deleted file mode 100755
index 9a8d0a15..00000000
--- a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the Caffe R-CNN ImageNet
-# for ILSVRC13 detection.
-
-MODEL=caffe_rcnn_imagenet_model
-CHECKSUM=42c1556d2d47a9128c4a90e0a9c5341c
-
-if [ -f $MODEL ]; then
-  echo "Model already exists. Checking md5..."
-  os=`uname -s`
-  if [ "$os" = "Linux" ]; then
-    checksum=`md5sum $MODEL | awk '{ print $1 }'`
-  elif [ "$os" = "Darwin" ]; then
-    checksum=`cat $MODEL | md5`
-  fi
-  if [ "$checksum" = "$CHECKSUM" ]; then
-    echo "Model checksum is correct. No need to download."
-    exit 0
-  else
-    echo "Model checksum is incorrect. Need to download again."
-  fi
-fi
-
-echo "Downloading..."
-
-wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
-
-echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/get_caffe_reference_imagenet_model.sh b/examples/imagenet/get_caffe_reference_imagenet_model.sh
deleted file mode 100755
index f687ebfa..00000000
--- a/examples/imagenet/get_caffe_reference_imagenet_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the caffe reference imagenet model
-# for ilsvrc image classification and deep feature extraction
-
-MODEL=caffe_reference_imagenet_model
-CHECKSUM=af678f0bd3cdd2437e35679d88665170
-
-if [ -f $MODEL ]; then
-  echo "Model already exists. Checking md5..."
-  os=`uname -s`
-  if [ "$os" = "Linux" ]; then
-    checksum=`md5sum $MODEL | awk '{ print $1 }'`
-  elif [ "$os" = "Darwin" ]; then
-    checksum=`cat $MODEL | md5`
-  fi
-  if [ "$checksum" = "$CHECKSUM" ]; then
-    echo "Model checksum is correct. No need to download."
-    exit 0
-  else
-    echo "Model checksum is incorrect. Need to download again."
-  fi
-fi
-
-echo "Downloading..."
-
-wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
-
-echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/imagenet_deploy.prototxt b/examples/imagenet/imagenet_deploy.prototxt
deleted file mode 100644
index 4e494f42..00000000
--- a/examples/imagenet/imagenet_deploy.prototxt
+++ /dev/null
@@ -1,212 +0,0 @@
-name: "CaffeNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8"
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8"
-  top: "prob"
-}
diff --git a/examples/imagenet/imagenet_solver.prototxt b/examples/imagenet/imagenet_solver.prototxt
deleted file mode 100644
index 5b5be4bb..00000000
--- a/examples/imagenet/imagenet_solver.prototxt
+++ /dev/null
@@ -1,14 +0,0 @@
-net: "examples/imagenet/imagenet_train_val.prototxt"
-test_iter: 1000
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 100000
-display: 20
-max_iter: 450000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/imagenet/caffe_imagenet"
-solver_mode: GPU
diff --git a/examples/imagenet/imagenet_train_val.prototxt b/examples/imagenet/imagenet_train_val.prototxt
deleted file mode 100644
index d6d64073..00000000
--- a/examples/imagenet/imagenet_train_val.prototxt
+++ /dev/null
@@ -1,344 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_leveldb"
-    batch_size: 256
-  }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
-  }
-  include: { phase: TRAIN }
-}
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_leveldb"
-    batch_size: 50
-  }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
-  include: { phase: TEST }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "fc8"
-  bottom: "label"
-  top: "accuracy"
-  include: { phase: TEST }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
diff --git a/examples/imagenet/rcnn_imagenet_deploy.prototxt b/examples/imagenet/rcnn_imagenet_deploy.prototxt
deleted file mode 100644
index ef75a0a5..00000000
--- a/examples/imagenet/rcnn_imagenet_deploy.prototxt
+++ /dev/null
@@ -1,207 +0,0 @@
-name: "R-CNN-ilsvrc13"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-# R-CNN classification layer made from R-CNN ILSVRC13 SVMs.
-layers {
-  name: "fc-rcnn"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc-rcnn"
-  inner_product_param {
-    num_output: 200
-  }
-}
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index b4a3110e..6b18407d 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -6,18 +6,16 @@ include_in_docs: true
 priority: 1
 ---
 
-Yangqing's Recipe on Brewing ImageNet
-=====================================
+Brewing ImageNet
+================
 
-    "All your braincells are belong to us."
-        - Caffeine
-
-We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf). Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming. If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](../../getting_pretrained_models.html) page. *Note that the pretrained model is for academic research / non-commercial use only*.
+We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf).
+Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming.
+If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](../../getting_pretrained_models.html) page.
+*Note that the pretrained model is for academic research / non-commercial use only*.
 
 To clarify, by ImageNet we actually mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.
 
-(If you don't get the quote, visit [Yann LeCun's fun page](http://yann.lecun.com/ex/fun/).
-
 Data Preparation
 ----------------
 
@@ -100,11 +98,13 @@ We all experience times when the power goes out, or we feel like rewarding ourse
 
     ./resume_training.sh
 
-where in the script `caffe_imagenet_train_1000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `imagenet_train_1000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
 Parting Words
 -------------
 
-Hope you liked this recipe! Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or finetuning the various parameters in the network. The recent ILSVRC 2013 challenge suggests that there are quite some room for improvement. **Caffe allows one to explore different network choices  more easily, by simply writing different prototxt files** - isn't that exciting?
+Hope you liked this recipe!
+Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or finetuning the various parameters in the network.
+**Caffe allows one to explore different network choices  more easily, by simply writing different prototxt files** - isn't that exciting?
 
-And since now you have a trained network, check out how to use it: [Running Pretrained ImageNet](../../getting_pretrained_models.html). This time we will use Python, but if you have wrappers for other languages, please kindly send a pull request!
+And since now you have a trained network, check out how to use it with the Python interface: [Running Pretrained ImageNet](../../getting_pretrained_models.html).