{"action":"created","issue":{"url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/62","repository_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn","labels_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/62/labels{/name}","comments_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/62/comments","events_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/62/events","html_url":"https://github.com/rbgirshick/py-faster-rcnn/issues/62","id":126558294,"number":62,"title":"ResNet Implementation for Faster-rcnn","user":{"login":"twtygqyy","id":11978373,"avatar_url":"https://avatars.githubusercontent.com/u/11978373?v=3","gravatar_id":"","url":"https://api.github.com/users/twtygqyy","html_url":"https://github.com/twtygqyy","followers_url":"https://api.github.com/users/twtygqyy/followers","following_url":"https://api.github.com/users/twtygqyy/following{/other_user}","gists_url":"https://api.github.com/users/twtygqyy/gists{/gist_id}","starred_url":"https://api.github.com/users/twtygqyy/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/twtygqyy/subscriptions","organizations_url":"https://api.github.com/users/twtygqyy/orgs","repos_url":"https://api.github.com/users/twtygqyy/repos","events_url":"https://api.github.com/users/twtygqyy/events{/privacy}","received_events_url":"https://api.github.com/users/twtygqyy/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":52,"created_at":"2016-01-14T01:46:19Z","updated_at":"2016-02-19T17:57:32Z","closed_at":null,"body":"Recently, I'm trying to combine ResNet network with Faster-rcnn. As the first step, I tried to train a model with ResNet 34 networks without bottleneck architectures. There is no error during training process, however, the detection result is very bad. I believe there is something wrong in my implementation, here is the prototxt I used for training, can anybody offer some help about how should I modify it?\r\n```Shell\r\nname: \"ResNet34\"\r\nlayer {\r\n  name: 'input-data'\r\n  type: 'Python'\r\n  top: 'data'\r\n  top: 'im_info'\r\n  top: 'gt_boxes'\r\n  python_param {\r\n    module: 'roi_data_layer.layer'\r\n    layer: 'RoIDataLayer'\r\n    param_str: \"'num_classes': 2\"\r\n  }\r\n}\r\n\r\n#conv1 7x7 64 /2\r\nlayer {\r\n  name: \"conv1\"\r\n  type: \"Convolution\"\r\n  bottom: \"data\"\r\n  top: \"conv1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 64\r\n    kernel_size: 7\r\n    pad: 1\r\n    stride: 2\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv1\"\r\n  top: \"conv1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv1_bn\"\r\n  top: \"conv1_bn\"\r\n}\r\nlayer {\r\n  name: \"pool1\"\r\n  type: \"Pooling\"\r\n  bottom: \"conv1_bn\"\r\n  top: \"pool1\"\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 3\r\n    stride: 2\r\n  }\r\n}\r\n\r\n#conv2_1 3x3 64\r\nlayer {\r\n  name: \"conv2_1_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"pool1\"\r\n  top: \"conv2_1_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 64\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_1_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv2_1_1\"\r\n  top: \"conv2_1_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_1_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv2_1_1_bn\"\r\n  top: \"conv2_1_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv2_1_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv2_1_1_bn\"\r\n  top: \"conv2_1_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 64\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_1_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv2_1_2\"\r\n  top: \"conv2_1_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_1_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"pool1\"\r\n  bottom: \"conv2_1_2_bn\"\r\n  top: \"conv2_1_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_1_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv2_1_sum\"\r\n  top: \"conv2_1_sum\"\r\n}\r\n\r\n#conv2_2 3x3 64\r\nlayer {\r\n  name: \"conv2_2_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv2_1_sum\"\r\n  top: \"conv2_2_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 64\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_2_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv2_2_1\"\r\n  top: \"conv2_2_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_2_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv2_2_1_bn\"\r\n  top: \"conv2_2_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv2_2_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv2_2_1_bn\"\r\n  top: \"conv2_2_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 64\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_2_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv2_2_2\"\r\n  top: \"conv2_2_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_2_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv2_1_sum\"\r\n  bottom: \"conv2_2_2_bn\"\r\n  top: \"conv2_2_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_2_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv2_2_sum\"\r\n  top: \"conv2_2_sum\"\r\n}\r\n\r\n#conv2_3 3x3 64\r\nlayer {\r\n  name: \"conv2_3_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv2_2_sum\"\r\n  top: \"conv2_3_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 64\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_3_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv2_3_1\"\r\n  top: \"conv2_3_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_3_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv2_3_1_bn\"\r\n  top: \"conv2_3_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv2_3_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv2_3_1_bn\"\r\n  top: \"conv2_3_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 64\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_3_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv2_3_2\"\r\n  top: \"conv2_3_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_3_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv2_2_sum\"\r\n  bottom: \"conv2_3_2_bn\"\r\n  top: \"conv2_3_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_3_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv2_3_sum\"\r\n  top: \"conv2_3_sum\"\r\n}\r\nlayer {\r\n  name: \"conv2_proj\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv2_3_sum\"\r\n  top: \"conv2_proj\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 1\r\n    pad: 0\r\n    stride: 2\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv2_proj_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv2_proj\"\r\n  top: \"conv2_proj_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\n\r\n#conv3_1 3x3 128\r\nlayer {\r\n  name: \"conv3_1_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv2_3_sum\"\r\n  top: \"conv3_1_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 2\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_1_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_1_1\"\r\n  top: \"conv3_1_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_1_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_1_1_bn\"\r\n  top: \"conv3_1_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv3_1_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_1_1_bn\"\r\n  top: \"conv3_1_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_1_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_1_2\"\r\n  top: \"conv3_1_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_1_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv2_proj_bn\"\r\n  bottom: \"conv3_1_2_bn\"\r\n  top: \"conv3_1_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_1_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_1_sum\"\r\n  top: \"conv3_1_sum\"\r\n}\r\n\r\n#conv3_2 3x3 128\r\nlayer {\r\n  name: \"conv3_2_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_1_sum\"\r\n  top: \"conv3_2_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_2_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_2_1\"\r\n  top: \"conv3_2_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_2_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_2_1_bn\"\r\n  top: \"conv3_2_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv3_2_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_2_1_bn\"\r\n  top: \"conv3_2_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_2_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_2_2\"\r\n  top: \"conv3_2_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_2_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv3_1_sum\"\r\n  bottom: \"conv3_2_2_bn\"\r\n  top: \"conv3_2_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_2_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_2_sum\"\r\n  top: \"conv3_2_sum\"\r\n}\r\n\r\n#conv3_3 3x3 128\r\nlayer {\r\n  name: \"conv3_3_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_2_sum\"\r\n  top: \"conv3_3_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_3_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_3_1\"\r\n  top: \"conv3_3_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_3_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_3_1_bn\"\r\n  top: \"conv3_3_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv3_3_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_3_1_bn\"\r\n  top: \"conv3_3_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_3_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_3_2\"\r\n  top: \"conv3_3_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_3_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv3_2_sum\"\r\n  bottom: \"conv3_3_2_bn\"\r\n  top: \"conv3_3_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_3_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_3_sum\"\r\n  top: \"conv3_3_sum\"\r\n}\r\n\r\n#conv3_4 3x3 128\r\nlayer {\r\n  name: \"conv3_4_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_3_sum\"\r\n  top: \"conv3_4_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_4_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_4_1\"\r\n  top: \"conv3_4_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_4_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_4_1_bn\"\r\n  top: \"conv3_4_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv3_4_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_4_1_bn\"\r\n  top: \"conv3_4_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 128\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_4_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_4_2\"\r\n  top: \"conv3_4_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_4_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv3_3_sum\"\r\n  bottom: \"conv3_4_2_bn\"\r\n  top: \"conv3_4_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_4_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv3_4_sum\"\r\n  top: \"conv3_4_sum\"\r\n}\r\nlayer {\r\n  name: \"conv3_proj\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_4_sum\"\r\n  top: \"conv3_proj\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 1\r\n    pad: 0\r\n    stride: 2\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv3_proj_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv3_proj\"\r\n  top: \"conv3_proj_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\n\r\n#conv4_1 3x3 256\r\nlayer {\r\n  name: \"conv4_1_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv3_4_sum\"\r\n  top: \"conv4_1_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 2\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_1_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_1_1\"\r\n  top: \"conv4_1_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_1_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_1_1_bn\"\r\n  top: \"conv4_1_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv4_1_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_1_1_bn\"\r\n  top: \"conv4_1_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_1_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_1_2\"\r\n  top: \"conv4_1_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_1_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv3_proj_bn\"\r\n  bottom: \"conv4_1_2_bn\"\r\n  top: \"conv4_1_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_1_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_1_sum\"\r\n  top: \"conv4_1_sum\"\r\n}\r\n\r\n#conv4_2 3x3 256\r\nlayer {\r\n  name: \"conv4_2_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_1_sum\"\r\n  top: \"conv4_2_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_2_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_2_1\"\r\n  top: \"conv4_2_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_2_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_2_1_bn\"\r\n  top: \"conv4_2_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv4_2_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_2_1_bn\"\r\n  top: \"conv4_2_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_2_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_2_2\"\r\n  top: \"conv4_2_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_2_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv4_1_sum\"\r\n  bottom: \"conv4_2_2_bn\"\r\n  top: \"conv4_2_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_2_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_2_sum\"\r\n  top: \"conv4_2_sum\"\r\n}\r\n\r\n#conv4_3 3x3 256\r\nlayer {\r\n  name: \"conv4_3_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_2_sum\"\r\n  top: \"conv4_3_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_3_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_3_1\"\r\n  top: \"conv4_3_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_3_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_3_1_bn\"\r\n  top: \"conv4_3_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv4_3_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_3_1_bn\"\r\n  top: \"conv4_3_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_3_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_3_2\"\r\n  top: \"conv4_3_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_3_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv4_2_sum\"\r\n  bottom: \"conv4_3_2_bn\"\r\n  top: \"conv4_3_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_3_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_3_sum\"\r\n  top: \"conv4_3_sum\"\r\n}\r\n\r\n#conv4_4 3x3 256\r\nlayer {\r\n  name: \"conv4_4_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_3_sum\"\r\n  top: \"conv4_4_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_4_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_4_1\"\r\n  top: \"conv4_4_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_4_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_4_1_bn\"\r\n  top: \"conv4_4_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv4_4_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_4_1_bn\"\r\n  top: \"conv4_4_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_4_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_4_2\"\r\n  top: \"conv4_4_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_4_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv4_3_sum\"\r\n  bottom: \"conv4_4_2_bn\"\r\n  top: \"conv4_4_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_4_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_4_sum\"\r\n  top: \"conv4_4_sum\"\r\n}\r\n\r\n#conv4_5 3x3 256\r\nlayer {\r\n  name: \"conv4_5_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_4_sum\"\r\n  top: \"conv4_5_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_5_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_5_1\"\r\n  top: \"conv4_5_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_5_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_5_1_bn\"\r\n  top: \"conv4_5_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv4_5_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_5_1_bn\"\r\n  top: \"conv4_5_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_5_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_5_2\"\r\n  top: \"conv4_5_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_5_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv4_4_sum\"\r\n  bottom: \"conv4_5_2_bn\"\r\n  top: \"conv4_5_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_5_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_5_sum\"\r\n  top: \"conv4_5_sum\"\r\n}\r\n\r\n#conv4_6 3x3 256\r\nlayer {\r\n  name: \"conv4_6_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_5_sum\"\r\n  top: \"conv4_6_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_6_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_6_1\"\r\n  top: \"conv4_6_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_6_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_6_1_bn\"\r\n  top: \"conv4_6_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv4_6_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_6_1_bn\"\r\n  top: \"conv4_6_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 256\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_6_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_6_2\"\r\n  top: \"conv4_6_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_6_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv4_5_sum\"\r\n  bottom: \"conv4_6_2_bn\"\r\n  top: \"conv4_6_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_6_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv4_6_sum\"\r\n  top: \"conv4_6_sum\"\r\n}\r\nlayer {\r\n  name: \"conv4_proj\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_6_sum\"\r\n  top: \"conv4_proj\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 1\r\n    pad: 0\r\n    stride: 2\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv4_proj_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv4_proj\"\r\n  top: \"conv4_proj_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\n\r\n#conv5_1 3x3 512\r\nlayer {\r\n  name: \"conv5_1_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv4_6_sum\"\r\n  top: \"conv5_1_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 2\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_1_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv5_1_1\"\r\n  top: \"conv5_1_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_1_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv5_1_1_bn\"\r\n  top: \"conv5_1_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv5_1_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv5_1_1_bn\"\r\n  top: \"conv5_1_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_1_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv5_1_2\"\r\n  top: \"conv5_1_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_1_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv4_proj_bn\"\r\n  bottom: \"conv5_1_2_bn\"\r\n  top: \"conv5_1_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_1_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv5_1_sum\"\r\n  top: \"conv5_1_sum\"\r\n}\r\n\r\n#conv5_2 3x3 512\r\nlayer {\r\n  name: \"conv5_2_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv5_1_sum\"\r\n  top: \"conv5_2_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_2_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv5_2_1\"\r\n  top: \"conv5_2_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_2_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv5_2_1_bn\"\r\n  top: \"conv5_2_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv5_2_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv5_2_1_bn\"\r\n  top: \"conv5_2_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_2_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv5_2_2\"\r\n  top: \"conv5_2_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_2_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv5_1_sum\"\r\n  bottom: \"conv5_2_2_bn\"\r\n  top: \"conv5_2_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_2_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv5_2_sum\"\r\n  top: \"conv5_2_sum\"\r\n}\r\n\r\n#conv5_3 3x3 512\r\nlayer {\r\n  name: \"conv5_3_1\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv5_2_sum\"\r\n  top: \"conv5_3_1\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_3_1_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv5_3_1\"\r\n  top: \"conv5_3_1_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_3_1_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv5_3_1_bn\"\r\n  top: \"conv5_3_1_bn\"\r\n}\r\nlayer {\r\n  name: \"conv5_3_2\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv5_3_1_bn\"\r\n  top: \"conv5_3_2\"\r\n  param {\r\n    lr_mult: 1\r\n    decay_mult: 1\r\n  }\r\n  param {\r\n     lr_mult: 2\r\n     decay_mult: 0\r\n  }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 3\r\n    pad: 1\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"msra\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_3_2_bn\"\r\n  type: \"BatchNorm\"\r\n  bottom: \"conv5_3_2\"\r\n  top: \"conv5_3_2_bn\"\r\n  batch_norm_param {\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_3_sum\"\r\n  type: \"Eltwise\"\r\n  bottom: \"conv5_2_sum\"\r\n  bottom: \"conv5_3_2_bn\"\r\n  top: \"conv5_3_sum\"\r\n  eltwise_param {\r\n    operation: SUM\r\n  }\r\n}\r\nlayer {\r\n  name: \"conv5_3_sum_relu\"\r\n  type: \"ReLU\"\r\n  bottom: \"conv5_3_sum\"\r\n  top: \"conv5_3_sum\"\r\n}\r\n\r\n#========= RPN ============\r\n\r\nlayer {\r\n  name: \"rpn_conv/3x3\"\r\n  type: \"Convolution\"\r\n  bottom: \"conv5_3_sum\"\r\n  top: \"rpn/output\"\r\n  param { lr_mult: 1.0 }\r\n  param { lr_mult: 2.0 }\r\n  convolution_param {\r\n    num_output: 512\r\n    kernel_size: 3 pad: 1 stride: 1\r\n    weight_filler { type: \"gaussian\" std: 0.01 }\r\n    bias_filler { type: \"constant\" value: 0 }\r\n  }\r\n}\r\nlayer {\r\n  name: \"rpn_relu/3x3\"\r\n  type: \"ReLU\"\r\n  bottom: \"rpn/output\"\r\n  top: \"rpn/output\"\r\n}\r\n\r\nlayer {\r\n  name: \"rpn_cls_score\"\r\n  type: \"Convolution\"\r\n  bottom: \"rpn/output\"\r\n  top: \"rpn_cls_score\"\r\n  param { lr_mult: 1.0 }\r\n  param { lr_mult: 2.0 }\r\n  convolution_param {\r\n    num_output: 18   # 2(bg/fg) * 9(anchors)\r\n    kernel_size: 1 pad: 0 stride: 1\r\n    weight_filler { type: \"gaussian\" std: 0.01 }\r\n    bias_filler { type: \"constant\" value: 0 }\r\n  }\r\n}\r\n\r\nlayer {\r\n  name: \"rpn_bbox_pred\"\r\n  type: \"Convolution\"\r\n  bottom: \"rpn/output\"\r\n  top: \"rpn_bbox_pred\"\r\n  param { lr_mult: 1.0 }\r\n  param { lr_mult: 2.0 }\r\n  convolution_param {\r\n    num_output: 36   # 4 * 9(anchors)\r\n    kernel_size: 1 pad: 0 stride: 1\r\n    weight_filler { type: \"gaussian\" std: 0.01 }\r\n    bias_filler { type: \"constant\" value: 0 }\r\n  }\r\n}\r\n\r\nlayer {\r\n   bottom: \"rpn_cls_score\"\r\n   top: \"rpn_cls_score_reshape\"\r\n   name: \"rpn_cls_score_reshape\"\r\n   type: \"Reshape\"\r\n   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }\r\n}\r\n\r\nlayer {\r\n  name: 'rpn-data'\r\n  type: 'Python'\r\n  bottom: 'rpn_cls_score'\r\n  bottom: 'gt_boxes'\r\n  bottom: 'im_info'\r\n  bottom: 'data'\r\n  top: 'rpn_labels'\r\n  top: 'rpn_bbox_targets'\r\n  top: 'rpn_bbox_inside_weights'\r\n  top: 'rpn_bbox_outside_weights'\r\n  python_param {\r\n    module: 'rpn.anchor_target_layer'\r\n    layer: 'AnchorTargetLayer'\r\n    param_str: \"'feat_stride': 16\"\r\n  }\r\n}\r\n\r\nlayer {\r\n  name: \"rpn_loss_cls\"\r\n  type: \"SoftmaxWithLoss\"\r\n  bottom: \"rpn_cls_score_reshape\"\r\n  bottom: \"rpn_labels\"\r\n  propagate_down: 1\r\n  propagate_down: 0\r\n  top: \"rpn_cls_loss\"\r\n  loss_weight: 1\r\n  loss_param {\r\n    ignore_label: -1\r\n    normalize: true\r\n  }\r\n}\r\n\r\nlayer {\r\n  name: \"rpn_loss_bbox\"\r\n  type: \"SmoothL1Loss\"\r\n  bottom: \"rpn_bbox_pred\"\r\n  bottom: \"rpn_bbox_targets\"\r\n  bottom: 'rpn_bbox_inside_weights'\r\n  bottom: 'rpn_bbox_outside_weights'\r\n  top: \"rpn_loss_bbox\"\r\n  loss_weight: 1\r\n  smooth_l1_loss_param { sigma: 3.0 }\r\n}\r\n\r\n#========= RoI Proposal ============\r\n\r\nlayer {\r\n  name: \"rpn_cls_prob\"\r\n  type: \"Softmax\"\r\n  bottom: \"rpn_cls_score_reshape\"\r\n  top: \"rpn_cls_prob\"\r\n}\r\n\r\nlayer {\r\n  name: 'rpn_cls_prob_reshape'\r\n  type: 'Reshape'\r\n  bottom: 'rpn_cls_prob'\r\n  top: 'rpn_cls_prob_reshape'\r\n  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }\r\n}\r\n\r\nlayer {\r\n  name: 'proposal'\r\n  type: 'Python'\r\n  bottom: 'rpn_cls_prob_reshape'\r\n  bottom: 'rpn_bbox_pred'\r\n  bottom: 'im_info'\r\n  top: 'rpn_rois'\r\n#  top: 'rpn_scores'\r\n  python_param {\r\n    module: 'rpn.proposal_layer'\r\n    layer: 'ProposalLayer'\r\n    param_str: \"'feat_stride': 16\"\r\n  }\r\n}\r\n\r\n#layer {\r\n#  name: 'debug-data'\r\n#  type: 'Python'\r\n#  bottom: 'data'\r\n#  bottom: 'rpn_rois'\r\n#  bottom: 'rpn_scores'\r\n#  python_param {\r\n#    module: 'rpn.debug_layer'\r\n#    layer: 'RPNDebugLayer'\r\n#  }\r\n#}\r\n\r\nlayer {\r\n  name: 'roi-data'\r\n  type: 'Python'\r\n  bottom: 'rpn_rois'\r\n  bottom: 'gt_boxes'\r\n  top: 'rois'\r\n  top: 'labels'\r\n  top: 'bbox_targets'\r\n  top: 'bbox_inside_weights'\r\n  top: 'bbox_outside_weights'\r\n  python_param {\r\n    module: 'rpn.proposal_target_layer'\r\n    layer: 'ProposalTargetLayer'\r\n    param_str: \"'num_classes': 2\"\r\n  }\r\n}\r\n\r\n#========= RCNN ============\r\n\r\nlayer {\r\n  name: \"roi_pool5\"\r\n  type: \"ROIPooling\"\r\n  bottom: \"conv5_3_sum\"\r\n  bottom: \"rois\"\r\n  top: \"pool5\"\r\n  roi_pooling_param {\r\n    pooled_w: 7\r\n    pooled_h: 7\r\n    spatial_scale: 0.0625 # 1/16\r\n  }\r\n}\r\nlayer {\r\n  name: \"cls_score\"\r\n  type: \"InnerProduct\"\r\n  bottom: \"pool5\"\r\n  top: \"cls_score\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  inner_product_param {\r\n    num_output: 2\r\n    weight_filler {\r\n      type: \"gaussian\"\r\n      std: 0.01\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"bbox_pred\"\r\n  type: \"InnerProduct\"\r\n  bottom: \"pool5\"\r\n  top: \"bbox_pred\"\r\n  param {\r\n    lr_mult: 1\r\n  }\r\n  param {\r\n    lr_mult: 2\r\n  }\r\n  inner_product_param {\r\n    num_output: 8\r\n    weight_filler {\r\n      type: \"gaussian\"\r\n      std: 0.001\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n      value: 0\r\n    }\r\n  }\r\n}\r\nlayer {\r\n  name: \"loss_cls\"\r\n  type: \"SoftmaxWithLoss\"\r\n  bottom: \"cls_score\"\r\n  bottom: \"labels\"\r\n  propagate_down: 1\r\n  propagate_down: 0\r\n  top: \"loss_cls\"\r\n  loss_weight: 1\r\n}\r\nlayer {\r\n  name: \"loss_bbox\"\r\n  type: \"SmoothL1Loss\"\r\n  bottom: \"bbox_pred\"\r\n  bottom: \"bbox_targets\"\r\n  bottom: \"bbox_inside_weights\"\r\n  bottom: \"bbox_outside_weights\"\r\n  top: \"loss_bbox\"\r\n  loss_weight: 1\r\n}\r\n\r\n```"},"comment":{"url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/comments/186332459","html_url":"https://github.com/rbgirshick/py-faster-rcnn/issues/62#issuecomment-186332459","issue_url":"https://api.github.com/repos/rbgirshick/py-faster-rcnn/issues/62","id":186332459,"user":{"login":"kaishijeng","id":12689630,"avatar_url":"https://avatars.githubusercontent.com/u/12689630?v=3","gravatar_id":"","url":"https://api.github.com/users/kaishijeng","html_url":"https://github.com/kaishijeng","followers_url":"https://api.github.com/users/kaishijeng/followers","following_url":"https://api.github.com/users/kaishijeng/following{/other_user}","gists_url":"https://api.github.com/users/kaishijeng/gists{/gist_id}","starred_url":"https://api.github.com/users/kaishijeng/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kaishijeng/subscriptions","organizations_url":"https://api.github.com/users/kaishijeng/orgs","repos_url":"https://api.github.com/users/kaishijeng/repos","events_url":"https://api.github.com/users/kaishijeng/events{/privacy}","received_events_url":"https://api.github.com/users/kaishijeng/received_events","type":"User","site_admin":false},"created_at":"2016-02-19T17:57:32Z","updated_at":"2016-02-19T17:57:32Z","body":"@siddharthm83, \r\n\r\n\r\n  I have a trained model based on your train.txt and like to try it with test. Since I didn't see response from twtyggyy, do you mind sharing your test.txt?\r\n\r\nThanks"}}