ParseNet on PASCAL 2012 dataset

readme.md

name	caffemodel	caffemodel_url	sha1	gist_id
ParseNet on PASCAL	VGG_VOC2012ext.caffemodel	http://cs.unc.edu/~wliu/projects/ParseNet/VGG_VOC2012ext.caffemodel	99cc76c373dc522fd70f80208b30a43ab2fba2f6

This is a model presented in the paper

ParseNet: Looking Wider to See Better
Wei Liu, Andrew Rabinovich, Alexander C. Berg
arXiv:1506.04579

This is the ParseNet model trained on PASCAL (using extra data from Hariharan et al. and finetuned from the fully convolutional reduced VGGNet).

You should be able to train/eval this model with http://github.com/weiliu89/caffe/tree/fcn. This branch introduces filter_stride (used for 'atrous' algorithm as described in Deeplab), L2-norm layer, evaluation code on the fly, etc.

The model should obtain 69.55 mean IoU on PASCAL 2012 segmementation val dataset. Please feel free to send me email (wliu@cs.unc.edu) if you have any questions.

VGG_VOC2012ext.prototxt

name: "VGG_VOC2012ext"
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_value: 104.00699
    mean_value: 116.66877
    mean_value: 122.67892
  }
  data_param {
    source: "examples/VOC2012ext/VOC2012ext_train_aug_img_lmdb"
    batch_size: 1
    backend: LMDB
  }
}
layer {
  name: "label"
  type: "Data"
  top: "label"
  include {
    phase: TRAIN
  }
  data_param {
    source: "examples/VOC2012ext/VOC2012ext_train_aug_label_lmdb"
    batch_size: 1
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TEST
  }
  transform_param {
    mean_value: 104.00699
    mean_value: 116.66877
    mean_value: 122.67892
  }
  data_param {
    source: "examples/VOC2012ext/VOC2012ext_val_img_lmdb"
    batch_size: 1
    backend: LMDB
  }
}
layer {
  name: "label"
  type: "Data"
  top: "label"
  include {
    phase: TEST
  }
  data_param {
    source: "examples/VOC2012ext/VOC2012ext_val_label_lmdb"
    batch_size: 1
    backend: LMDB
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 2
    filter_stride: 2
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 2
    filter_stride: 2
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 2
    filter_stride: 2
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "fc6"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1024
    kernel_size: 3
    filter_stride: 12
    pad: 12
    engine: CAFFE
  }
}
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: "Convolution"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1024
    kernel_size: 1
    engine: CAFFE
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
### pool ###
layer {
  name: "fc7_norm"
  type: "Normalize"
  bottom: "fc7"
  top: "fc7_norm"
  norm_param {
    scale_filler {
      type: "constant"
      value: 10
    }
    across_spatial: false
    channel_shared: false
    fix_scale: false
  }
}
layer {
  name: "pool6_1x1"
  type: "Pooling"
  bottom: "fc7"
  top: "pool6_1x1"
  pooling_param {
    pool: AVE
    bin_size: 1
  }
}
layer {
  name: "pool6_1x1_norm"
  type: "Normalize"
  bottom: "pool6_1x1"
  top: "pool6_1x1_norm"
  norm_param {
    scale_filler {
      type: "constant"
      value: 10
    }
    across_spatial: false
    channel_shared: false
    fix_scale: false
  }
}
layer {
  name: "pool6_1x1_norm_drop"
  type: "Dropout"
  bottom: "pool6_1x1_norm"
  top: "pool6_1x1_norm"
  dropout_param {
    dropout_ratio: 0.3
  }
}
layer {
  name: "fc7_norm_score21"
  type: "Convolution"
  bottom: "fc7_norm"
  top: "fc7_norm_score21"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: CAFFE
  }
}
layer {
  name: "pool6_1x1_norm_score21"
  type: "Convolution"
  bottom: "pool6_1x1_norm"
  top: "pool6_1x1_norm_score21"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: CAFFE
  }
}
layer {
  name: "pool6_1x1_norm_upscore21"
  type: "UnPooling"
  bottom: "pool6_1x1_norm_score21"
  bottom: "fc7_norm_score21"
  top: "pool6_1x1_norm_upscore21"
  unpooling_param {
    unpool: REP
    out_kernel_size: 0
    out_stride: 0
  }
}
layer {
  name: "score21"
  type: "Eltwise"
  bottom: "pool6_1x1_norm_upscore21"
  bottom: "fc7_norm_score21"
  top: "score21"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "upscore21"
  type: "Deconvolution"
  bottom: "score21"
  top: "upscore21"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 16
    stride: 8
    pad: 4
    group: 21
    weight_filler {
      type: "bilinear_upsampling"
    }
  }
}
layer {
  type: "Crop"
  name: "score"
  bottom: "upscore21"
  bottom: "data"
  top: "score"
}
layer {
  type: 'SoftmaxWithLoss'
  name: 'loss'
  bottom: 'score'
  bottom: 'label'
  top: 'loss'
  loss_param {
    normalize: false
    ignore_label: 255
  }
  include {
    phase: TRAIN
  }
}
layer {
  type: "ParseOutput"
  name: "predlabel"
  bottom: "score"
  top: "predlabel"
  include {
    phase: TEST
  }
}
layer {
  type: "ParseEvaluate"
  name: "evaluation"
  bottom: "predlabel"
  bottom: "label"
  top: "evaluation"
  parse_evaluate_param {
    num_labels: 21
    ignore_label: 255
  }
  include {
    phase: TEST
  }
}

VGG_VOC2012ext.sh

# You should change $CAFFE_ROOT to the caffe root directory (e.g. ~/project/caffe) or make it as environment variable (e.g. put it in ~/.bashrc)
cd $CAFFE_ROOT
if [ ! -d jobs/VGGNet/VOC2012ext ]
then
  mkdir -p jobs/VGGNet/VOC2012ext
fi
./build/tools/caffe train \
--solver="models/VGGNet/VOC2012ext/VGG_VOC2012ext_solver.prototxt" \
--weights="models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel" \
--gpu 0 2>&1 | tee jobs/VGGNet/VOC2012ext/VGG_VOC2012ext.log

VGG_VOC2012ext_solver.prototxt

net: "models/VGGNet/VOC2012ext/VGG_VOC2012ext.prototxt"
test_iter: 1449
test_interval: 2000
eval_type: "segmentation"
display: 20
average_loss: 20
lr_policy: "poly"
power: 0.9
base_lr: 1e-8
momentum: 0.9
iter_size: 8
max_iter: 20000
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "models/VGGNet/VOC2012ext/VGG_VOC2012ext"
test_initialization: false
debug_info: false

Hello, what is the licensing for this model? Thanks