FCN-16s Fully Convolutional Semantic Segmentation on PASCAL

readme.md

name	caffemodel	caffemodel_url	sha1	gist_id
FCN-16s Fully Convolutional Semantic Segmentation on PASCAL	fcn-16s-pascal.caffemodel	http://dl.caffe.berkeleyvision.org/fcn-16s-pascal.caffemodel	f5717bcc805976be1c3343342d9b2434145b9053	d24098e083bec05e456e

This is a model from the paper:

Fully Convolutional Networks for Semantic Segmentation
Jonathan Long, Evan Shelhamer, Trevor Darrell
arXiv:1411.4038

This is the two stream, 16 pixel prediction stride version.

This model was trained for the 21-class (including background) PASCAL VOC segmentation task. The final layer outputs scores for each class, which may be normalized via softmax or argmaxed to obtain per-pixel labels. The first label (index zero) is background, with the rest following the standard alphabetical ordering.

The input is expected in BGR channel order, with the following per-channel mean subtracted:

B 104.00698793 G 116.66876762 R 122.67891434

This is a pre-release: it requires unmerged PRs to run. It should be usable with the branch available at https://github.com/longjon/caffe/tree/future. Training ought to be possible with that code, but the original training scripts have not yet been ported.

This model obtains 67.1 mean I/U on PASCAL seg val 2011.

fcn-16s-pascal-deploy.prototxt

name: 'FCN'

input: 'data'
input_dim: 1
input_dim: 3
input_dim: 500
input_dim: 500
force_backward: true

layers { bottom: 'data' top: 'conv1_1' name: 'conv1_1' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 64 pad: 100 kernel_size: 3 } }
layers { bottom: 'conv1_1' top: 'conv1_1' name: 'relu1_1' type: RELU }
layers { bottom: 'conv1_1' top: 'conv1_2' name: 'conv1_2' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 64 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv1_2' top: 'conv1_2' name: 'relu1_2' type: RELU }
layers { name: 'pool1' bottom: 'conv1_2' top: 'pool1' type: POOLING
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layers { name: 'conv2_1' bottom: 'pool1' top: 'conv2_1' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv2_1' top: 'conv2_1' name: 'relu2_1' type: RELU }
layers { bottom: 'conv2_1' top: 'conv2_2' name: 'conv2_2' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv2_2' top: 'conv2_2' name: 'relu2_2' type: RELU }
layers { bottom: 'conv2_2' top: 'pool2' name: 'pool2' type: POOLING
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layers { bottom: 'pool2' top: 'conv3_1' name: 'conv3_1' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv3_1' top: 'conv3_1' name: 'relu3_1' type: RELU }
layers { bottom: 'conv3_1' top: 'conv3_2' name: 'conv3_2' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv3_2' top: 'conv3_2' name: 'relu3_2' type: RELU }
layers { bottom: 'conv3_2' top: 'conv3_3' name: 'conv3_3' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv3_3' top: 'conv3_3' name: 'relu3_3' type: RELU }
layers { bottom: 'conv3_3' top: 'pool3' name: 'pool3' type: POOLING
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layers { bottom: 'pool3' top: 'conv4_1' name: 'conv4_1' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv4_1' top: 'conv4_1' name: 'relu4_1' type: RELU }
layers { bottom: 'conv4_1' top: 'conv4_2' name: 'conv4_2' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv4_2' top: 'conv4_2' name: 'relu4_2' type: RELU }
layers { bottom: 'conv4_2' top: 'conv4_3' name: 'conv4_3' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv4_3' top: 'conv4_3' name: 'relu4_3' type: RELU }
layers { bottom: 'conv4_3' top: 'pool4' name: 'pool4' type: POOLING
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layers { bottom: 'pool4' top: 'conv5_1' name: 'conv5_1' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv5_1' top: 'conv5_1' name: 'relu5_1' type: RELU }
layers { bottom: 'conv5_1' top: 'conv5_2' name: 'conv5_2' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv5_2' top: 'conv5_2' name: 'relu5_2' type: RELU }
layers { bottom: 'conv5_2' top: 'conv5_3' name: 'conv5_3' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layers { bottom: 'conv5_3' top: 'conv5_3' name: 'relu5_3' type: RELU }
layers { bottom: 'conv5_3' top: 'pool5' name: 'pool5' type: POOLING
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layers { bottom: 'pool5' top: 'fc6' name: 'fc6' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE kernel_size: 7 num_output: 4096 } }
layers { bottom: 'fc6' top: 'fc6' name: 'relu6' type: RELU }
layers { bottom: 'fc6' top: 'fc6' name: 'drop6' type: DROPOUT
  dropout_param { dropout_ratio: 0.5 } }
layers { bottom: 'fc6' top: 'fc7' name: 'fc7' type: CONVOLUTION
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE kernel_size: 1 num_output: 4096 } }
layers { bottom: 'fc7' top: 'fc7' name: 'relu7' type: RELU }
layers { bottom: 'fc7' top: 'fc7' name: 'drop7' type: DROPOUT
  dropout_param { dropout_ratio: 0.5 } }
layers { name: 'score-fr' type: CONVOLUTION bottom: 'fc7' top: 'score'
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 21 kernel_size: 1 } }

layers { type: DECONVOLUTION name: 'score2' bottom: 'score' top: 'score2'
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { kernel_size: 4 stride: 2 num_output: 21 } }

layers { name: 'score-pool4' type: CONVOLUTION bottom: 'pool4' top: 'score-pool4'
  blobs_lr: 1 blobs_lr: 2 weight_decay: 1 weight_decay: 0
  convolution_param { engine: CAFFE num_output: 21 kernel_size: 1 } }

layers { type: CROP name: 'crop' bottom: 'score-pool4' bottom: 'score2'
  top: 'score-pool4c' }

layers { type: ELTWISE name: 'fuse' bottom: 'score2' bottom: 'score-pool4c' top: 'score-fused'
  eltwise_param { operation: SUM } }

layers { type: DECONVOLUTION name: 'upsample-new'
  bottom: 'score-fused' top: 'bigscore'
  blobs_lr: 0 blobs_lr: 0
  convolution_param { num_output: 21 kernel_size: 32 stride: 16 } }

layers { type: CROP name: 'crop' bottom: 'bigscore' bottom: 'data' top: 'upscore' }