The model spec for the baseline CNN Model on WIDER dataset

1 - WIDER Baseline CNN.md

This gist holds the model spec for the baseline CNN model on the WIDER dataset.

The CNN structure is AlexNet. Network parameters are initialized using a model pretrained on ImageNet.

The weights can be downloaded at

cuhk_wider_baseline_cnn.caffemodel

Please refer to

WIDER Dataset

and

the CVPR'15 paper

for more details

cuhk_wider_baseline_cnn_deploy.txt

#Note: This model has the same sturcture of the AlexNet
name: "WIDER_Baseline_CNN"
input: "data"
input_dim: [1,3,224,224]
layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1"
  convolution_param {num_output: 96 kernel_size: 11 stride: 4}}
layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1"}
layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1"
  pooling_param {pool: MAX kernel_size: 3 stride: 2}}
layer { name: "norm1" type: "LRN" bottom: "pool1" top: "norm1"
  lrn_param {local_size: 5 alpha: 0.0001 beta: 0.75}}
layer { name: "conv2" type: "Convolution" bottom: "norm1" top: "conv2"
 convolution_param {num_output: 256 pad: 2 kernel_size: 5 group: 2}}
layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2"}
layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2"
  pooling_param {pool: MAX kernel_size: 3 stride: 2}}
layer { name: "norm2" type: "LRN" bottom: "pool2" top: "norm2"
  lrn_param {local_size: 5 alpha: 0.0001 beta: 0.75}}
layer { name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3"
 convolution_param {num_output: 384 pad: 1 kernel_size: 3}}
layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3"}
layer { name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4"
 convolution_param {num_output: 384 pad: 1 kernel_size: 3 group: 2}}
layer { name: "relu4" type: "ReLU" bottom: "conv4" top: "conv4"}
layer { name: "conv5" type: "Convolution" bottom: "conv4" top: "conv5"
 convolution_param {num_output: 256 pad: 1 kernel_size: 3 group: 2}}
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"
  inner_product_param {num_output: 4096}}
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"
 inner_product_param {num_output: 4096}}
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_event" type: "InnerProduct" bottom: "fc7" top: "fc8"
  inner_product_param {num_output: 61}}
layer { name: "prob" type: "Softmax" bottom: "fc8" top: "prob_raw"}