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NVIDIA / DIGITS

Deep Learning GPU Training System
https://developer.nvidia.com/digits
BSD 3-Clause "New" or "Revised" License
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DetectNet + MSCOCO not working #1655

Open antoniodourado opened 7 years ago

antoniodourado commented 7 years ago

Hey there.

I'm trying to train a DetectNet based on dusty example with MSCOCO. I developed a python script to convert COCO annotations to KITTI format and the built the dataset normally. I used dusty-nv example as basis: https://github.com/dusty-nv/jetson-inference

I adapted the network as follows:

_name: "DetectNet" layer { name: "train_data" type: "Data" top: "data" data_param { backend: LMDB source: "/media/antonio/LinuxOutro/DIGITS/digits/jobs/20170522-184156-7112/train_db/features/data.mdb" batch_size: 10 } include: { phase: TRAIN } } layer { name: "train_label" type: "Data" top: "label" data_param { backend: LMDB source: "/media/antonio/LinuxOutro/DIGITS/digits/jobs/20170522-184156-7112/train_db/labels/data.mdb" batch_size: 10 } include: { phase: TRAIN } } layer { name: "val_data" type: "Data" top: "data" data_param { backend: LMDB source: "/media/antonio/LinuxOutro/DIGITS/digits/jobs/20170522-184156-7112/val_db/features/data.mdb" batch_size: 6 } include: { phase: TEST stage: "val" } } layer { name: "val_label" type: "Data" top: "label" data_param { backend: LMDB source: "/media/antonio/LinuxOutro/DIGITS/digits/jobs/20170522-184156-7112/val_db/labels/data.mdb" batch_size: 6 } include: { phase: TEST stage: "val" } } layer { name: "deploy_data" type: "Input" top: "data" input_param { shape { dim: 1 dim: 3 dim: 640 dim: 640 } } include: { phase: TEST notstage: "val" } }

The LMDB source I pointed to the "data.mdb" files generated by Digits. I couldn't find the lmdb files and considered that these were correct. Is it?

It took 24 hours to train and it's not working at all with mAP zero. It doest give me any bbox even when I test with the train images: results

Here are the parameters for model training: configs

Here an example of image with the following label: sink 0 0 0 0.0 346.43 180.78 79.66 0 0 0 0 0 0 0 bowl 0 0 0 359.22 385.68 24.07 19.57 0 0 0 0 0 0 0

Edit1: I noticed that my labels are formatted as following: sink 0 0 0 X Y WIDTH HEIGHT 0 0 0 0 0 0 0 Should I format like this? sink 0 0 0 X Y X+WIDTH Y+HEIGHT 0 0 0 0 0 0 0

When I submit it to test for detecting with bboxes: zeros

Obviously I messed something up, can you guys help me out here?

ontheway16 commented 7 years ago

Hello, yes it doesn't work with "width" etc. You have to use xmin, ymin and xmax, ymax. Means; x, y, x+width, y+height, as you stated above.

antoniodourado commented 7 years ago

Thank you for you reply.

Also I tried to train the whole MSCOCO dataset with all those classes and I saw in another post that the default detectnet prototxt is single class only. Probably it's also an issue, right? I must add the new classes into my prototxt. Btw, do you know how the "src" and "dst" fields work in prototxt "object_detection" like this? object_class: { src: 1 dst: 0} # obj class 1 -> cvg index 0 What exactly is the cvg index?

ontheway16 commented 7 years ago

You may experiment with the following, https://github.com/NVIDIA/caffe/blob/caffe-0.15/examples/kitti/detectnet_network-2classes.prototxt

But, for multiple classes, things are not so easy with detectnet, if you have more than 10-20 classes. Even with 2-3 classes, I recommend them train individually first, then after ensuring it is capable to reach certain mAP levels, start increasing class numbers one at a time. It would be great if Nvidia decides to add another detection model supporting multi classes easier way.

antoniodourado commented 7 years ago

Yes, I will test for single and two classes.

On these lines, what defines src and dst? Because on single class, src meant obj class and now dst means obj class. The "src: 8" is what really gets me. Don't know what defined the number 8. object_class: { src: 1 dst: 0} # cars -> 0 object_class: { src: 8 dst: 1} # pedestrians -> 1

ontheway16 commented 7 years ago

Please check the following; https://github.com/NVIDIA/caffe/pull/157 https://github.com/NVIDIA/DIGITS/tree/digits-4.0/digits/extensions/data/objectDetection#custom-class-mappings

antoniodourado commented 7 years ago

That's very nice! I will train a single and double class detectnet and post the results here in a few days. Have someone tested a detectnet with 10 or 15 classes and reported the performance?

Thank you so much for now.

antoniodourado commented 7 years ago

Hello, @ontheway16. I managed to get the single class working with custom mappings. However, I tried to apply it to the two class example and got the following error:

err1

I built the Dataset as follows:

ovenperson

And here is my network. I adapted it for two classes:

# DetectNet network

# Data/Input layers
name: "DetectNet"
layer {
  name: "train_data"
  type: "Data"
  top: "data"
  data_param {
    backend: LMDB
    source: "/jobs/20170530-141649-95c1/train_db/features"
    batch_size: 10
  }
  include: { phase: TRAIN }
}
layer {
  name: "train_label"
  type: "Data"
  top: "label"
  data_param {
    backend: LMDB
    source: "/jobs/20170530-141649-95c1/train_db/labels"
    batch_size: 10
  }
  include: { phase: TRAIN }
}
layer {
  name: "val_data"
  type: "Data"
  top: "data"
  data_param {
    backend: LMDB
    source: "/jobs/20170530-141649-95c1/val_db/features"
    batch_size: 6
  }
  include: { phase: TEST stage: "val" }
}
layer {
  name: "val_label"
  type: "Data"
  top: "label"
  data_param {
    backend: LMDB
    source: "/jobs/20170530-141649-95c1/val_db/labels"
    batch_size: 6
  }
  include: { phase: TEST stage: "val" }
}
layer {
  name: "deploy_data"
  type: "Input"
  top: "data"
  input_param {
    shape {
      dim: 1
      dim: 3
      dim: 640
      dim: 640
    }
  }
  include: { phase: TEST not_stage: "val" }
}

# Data transformation layers
layer {
  name: "train_transform"
  type: "DetectNetTransformation"
  bottom: "data"
  bottom: "label"
  top: "transformed_data"
  top: "transformed_label"
  detectnet_groundtruth_param: {
    stride: 16
    scale_cvg: 0.4
    gridbox_type: GRIDBOX_MIN
    coverage_type: RECTANGULAR
    min_cvg_len: 20
    obj_norm: true
    image_size_x: 640
    image_size_y: 640
    crop_bboxes: false
    object_class: { src: 1 dst: 0} # obj class 1 -> cvg index 0
    object_class: { src: 2 dst: 1} # obj class 2 -> cvg index 1
  }
   detectnet_augmentation_param: {
    crop_prob: 1
    shift_x: 32
    shift_y: 32
    flip_prob: 0.5
    rotation_prob: 0
    max_rotate_degree: 5
    scale_prob: 0.4
    scale_min: 0.8
    scale_max: 1.2
    hue_rotation_prob: 0.8
    hue_rotation: 30
    desaturation_prob: 0.8
    desaturation_max: 0.8
  }
  transform_param: {
    mean_value: 127
  }
  include: { phase: TRAIN }
}
layer {
  name: "val_transform"
  type: "DetectNetTransformation"
  bottom: "data"
  bottom: "label"
  top: "transformed_data"
  top: "transformed_label"
  detectnet_groundtruth_param: {
    stride: 16
    scale_cvg: 0.4
    gridbox_type: GRIDBOX_MIN
    coverage_type: RECTANGULAR
    min_cvg_len: 20
    obj_norm: true
    image_size_x: 640
    image_size_y: 640
    crop_bboxes: false
    object_class: { src: 1 dst: 0} # obj class 1 -> cvg index 0
    object_class: { src: 2 dst: 1} # obj class 2 -> cvg index 1
  }
  transform_param: {
    mean_value: 127
  }
  include: { phase: TEST stage: "val" }
}
layer {
  name: "deploy_transform"
  type: "Power"
  bottom: "data"
  top: "transformed_data"
  power_param {
    shift: -127
  }
  include: { phase: TEST not_stage: "val" }
}

# Label conversion layers
layer {
  name: "slice-label"
  type: "Slice"
  bottom: "transformed_label"
  top: "foreground-label"
  top: "bbox-label"
  top: "size-label"
  top: "obj-label"
  top: "coverage-label"
  slice_param {
    slice_dim: 1
    slice_point: 1
    slice_point: 5
    slice_point: 7
    slice_point: 8
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "coverage-block"
  type: "Concat"
  bottom: "foreground-label"
  bottom: "foreground-label"
  bottom: "foreground-label"
  bottom: "foreground-label"
  top: "coverage-block"
  concat_param {
    concat_dim: 1
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "size-block"
  type: "Concat"
  bottom: "size-label"
  bottom: "size-label"
  top: "size-block"
  concat_param {
    concat_dim: 1
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "obj-block"
  type: "Concat"
  bottom: "obj-label"
  bottom: "obj-label"
  bottom: "obj-label"
  bottom: "obj-label"
  top: "obj-block"
  concat_param {
    concat_dim: 1
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "bb-label-norm"
  type: "Eltwise"
  bottom: "bbox-label"
  bottom: "size-block"
  top: "bbox-label-norm"
  eltwise_param {
    operation: PROD
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "bb-obj-norm"
  type: "Eltwise"
  bottom: "bbox-label-norm"
  bottom: "obj-block"
  top: "bbox-obj-label-norm"
  eltwise_param {
    operation: PROD
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}

######################################################################
# Start of convolutional network
######################################################################

layer {
  name: "conv1/7x7_s2"
  type: "Convolution"
  bottom: "transformed_data"
  top: "conv1/7x7_s2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "conv1/relu_7x7"
  type: "ReLU"
  bottom: "conv1/7x7_s2"
  top: "conv1/7x7_s2"
}

layer {
  name: "pool1/3x3_s2"
  type: "Pooling"
  bottom: "conv1/7x7_s2"
  top: "pool1/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "pool1/norm1"
  type: "LRN"
  bottom: "pool1/3x3_s2"
  top: "pool1/norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}

layer {
  name: "conv2/3x3_reduce"
  type: "Convolution"
  bottom: "pool1/norm1"
  top: "conv2/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "conv2/relu_3x3_reduce"
  type: "ReLU"
  bottom: "conv2/3x3_reduce"
  top: "conv2/3x3_reduce"
}

layer {
  name: "conv2/3x3"
  type: "Convolution"
  bottom: "conv2/3x3_reduce"
  top: "conv2/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "conv2/relu_3x3"
  type: "ReLU"
  bottom: "conv2/3x3"
  top: "conv2/3x3"
}

layer {
  name: "conv2/norm2"
  type: "LRN"
  bottom: "conv2/3x3"
  top: "conv2/norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}

layer {
  name: "pool2/3x3_s2"
  type: "Pooling"
  bottom: "conv2/norm2"
  top: "pool2/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "inception_3a/1x1"
  type: "Convolution"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_3a/relu_1x1"
  type: "ReLU"
  bottom: "inception_3a/1x1"
  top: "inception_3a/1x1"
}

layer {
  name: "inception_3a/3x3_reduce"
  type: "Convolution"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.09
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_3a/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_3a/3x3_reduce"
  top: "inception_3a/3x3_reduce"
}

layer {
  name: "inception_3a/3x3"
  type: "Convolution"
  bottom: "inception_3a/3x3_reduce"
  top: "inception_3a/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_3a/relu_3x3"
  type: "ReLU"
  bottom: "inception_3a/3x3"
  top: "inception_3a/3x3"
}

layer {
  name: "inception_3a/5x5_reduce"
  type: "Convolution"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.2
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_3a/5x5_reduce"
  top: "inception_3a/5x5_reduce"
}
layer {
  name: "inception_3a/5x5"
  type: "Convolution"
  bottom: "inception_3a/5x5_reduce"
  top: "inception_3a/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_5x5"
  type: "ReLU"
  bottom: "inception_3a/5x5"
  top: "inception_3a/5x5"
}

layer {
  name: "inception_3a/pool"
  type: "Pooling"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}

layer {
  name: "inception_3a/pool_proj"
  type: "Convolution"
  bottom: "inception_3a/pool"
  top: "inception_3a/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_3a/pool_proj"
  top: "inception_3a/pool_proj"
}

layer {
  name: "inception_3a/output"
  type: "Concat"
  bottom: "inception_3a/1x1"
  bottom: "inception_3a/3x3"
  bottom: "inception_3a/5x5"
  bottom: "inception_3a/pool_proj"
  top: "inception_3a/output"
}

layer {
  name: "inception_3b/1x1"
  type: "Convolution"
  bottom: "inception_3a/output"
  top: "inception_3b/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_3b/relu_1x1"
  type: "ReLU"
  bottom: "inception_3b/1x1"
  top: "inception_3b/1x1"
}

layer {
  name: "inception_3b/3x3_reduce"
  type: "Convolution"
  bottom: "inception_3a/output"
  top: "inception_3b/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.09
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_3b/3x3_reduce"
  top: "inception_3b/3x3_reduce"
}
layer {
  name: "inception_3b/3x3"
  type: "Convolution"
  bottom: "inception_3b/3x3_reduce"
  top: "inception_3b/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_3x3"
  type: "ReLU"
  bottom: "inception_3b/3x3"
  top: "inception_3b/3x3"
}

layer {
  name: "inception_3b/5x5_reduce"
  type: "Convolution"
  bottom: "inception_3a/output"
  top: "inception_3b/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.2
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_3b/5x5_reduce"
  top: "inception_3b/5x5_reduce"
}
layer {
  name: "inception_3b/5x5"
  type: "Convolution"
  bottom: "inception_3b/5x5_reduce"
  top: "inception_3b/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_5x5"
  type: "ReLU"
  bottom: "inception_3b/5x5"
  top: "inception_3b/5x5"
}

layer {
  name: "inception_3b/pool"
  type: "Pooling"
  bottom: "inception_3a/output"
  top: "inception_3b/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_3b/pool_proj"
  type: "Convolution"
  bottom: "inception_3b/pool"
  top: "inception_3b/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_3b/pool_proj"
  top: "inception_3b/pool_proj"
}
layer {
  name: "inception_3b/output"
  type: "Concat"
  bottom: "inception_3b/1x1"
  bottom: "inception_3b/3x3"
  bottom: "inception_3b/5x5"
  bottom: "inception_3b/pool_proj"
  top: "inception_3b/output"
}

layer {
  name: "pool3/3x3_s2"
  type: "Pooling"
  bottom: "inception_3b/output"
  top: "pool3/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "inception_4a/1x1"
  type: "Convolution"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_4a/relu_1x1"
  type: "ReLU"
  bottom: "inception_4a/1x1"
  top: "inception_4a/1x1"
}

layer {
  name: "inception_4a/3x3_reduce"
  type: "Convolution"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.09
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_4a/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4a/3x3_reduce"
  top: "inception_4a/3x3_reduce"
}

layer {
  name: "inception_4a/3x3"
  type: "Convolution"
  bottom: "inception_4a/3x3_reduce"
  top: "inception_4a/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 208
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_4a/relu_3x3"
  type: "ReLU"
  bottom: "inception_4a/3x3"
  top: "inception_4a/3x3"
}

layer {
  name: "inception_4a/5x5_reduce"
  type: "Convolution"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.2
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4a/5x5_reduce"
  top: "inception_4a/5x5_reduce"
}
layer {
  name: "inception_4a/5x5"
  type: "Convolution"
  bottom: "inception_4a/5x5_reduce"
  top: "inception_4a/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_5x5"
  type: "ReLU"
  bottom: "inception_4a/5x5"
  top: "inception_4a/5x5"
}
layer {
  name: "inception_4a/pool"
  type: "Pooling"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4a/pool_proj"
  type: "Convolution"
  bottom: "inception_4a/pool"
  top: "inception_4a/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4a/pool_proj"
  top: "inception_4a/pool_proj"
}
layer {
  name: "inception_4a/output"
  type: "Concat"
  bottom: "inception_4a/1x1"
  bottom: "inception_4a/3x3"
  bottom: "inception_4a/5x5"
  bottom: "inception_4a/pool_proj"
  top: "inception_4a/output"
}

layer {
  name: "inception_4b/1x1"
  type: "Convolution"
  bottom: "inception_4a/output"
  top: "inception_4b/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_4b/relu_1x1"
  type: "ReLU"
  bottom: "inception_4b/1x1"
  top: "inception_4b/1x1"
}
layer {
  name: "inception_4b/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4a/output"
  top: "inception_4b/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 112
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.09
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4b/3x3_reduce"
  top: "inception_4b/3x3_reduce"
}
layer {
  name: "inception_4b/3x3"
  type: "Convolution"
  bottom: "inception_4b/3x3_reduce"
  top: "inception_4b/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 224
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_3x3"
  type: "ReLU"
  bottom: "inception_4b/3x3"
  top: "inception_4b/3x3"
}
layer {
  name: "inception_4b/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4a/output"
  top: "inception_4b/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.2
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4b/5x5_reduce"
  top: "inception_4b/5x5_reduce"
}
layer {
  name: "inception_4b/5x5"
  type: "Convolution"
  bottom: "inception_4b/5x5_reduce"
  top: "inception_4b/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_5x5"
  type: "ReLU"
  bottom: "inception_4b/5x5"
  top: "inception_4b/5x5"
}
layer {
  name: "inception_4b/pool"
  type: "Pooling"
  bottom: "inception_4a/output"
  top: "inception_4b/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4b/pool_proj"
  type: "Convolution"
  bottom: "inception_4b/pool"
  top: "inception_4b/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4b/pool_proj"
  top: "inception_4b/pool_proj"
}
layer {
  name: "inception_4b/output"
  type: "Concat"
  bottom: "inception_4b/1x1"
  bottom: "inception_4b/3x3"
  bottom: "inception_4b/5x5"
  bottom: "inception_4b/pool_proj"
  top: "inception_4b/output"
}

layer {
  name: "inception_4c/1x1"
  type: "Convolution"
  bottom: "inception_4b/output"
  top: "inception_4c/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_4c/relu_1x1"
  type: "ReLU"
  bottom: "inception_4c/1x1"
  top: "inception_4c/1x1"
}

layer {
  name: "inception_4c/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4b/output"
  top: "inception_4c/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.09
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

layer {
  name: "inception_4c/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4c/3x3_reduce"
  top: "inception_4c/3x3_reduce"
}
layer {
  name: "inception_4c/3x3"
  type: "Convolution"
  bottom: "inception_4c/3x3_reduce"
  top: "inception_4c/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_3x3"
  type: "ReLU"
  bottom: "inception_4c/3x3"
  top: "inception_4c/3x3"
}
layer {
  name: "inception_4c/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4b/output"
  top: "inception_4c/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.2
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4c/5x5_reduce"
  top: "inception_4c/5x5_reduce"
}
layer {
  name: "inception_4c/5x5"
  type: "Convolution"
  bottom: "inception_4c/5x5_reduce"
  top: "inception_4c/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_5x5"
  type: "ReLU"
  bottom: "inception_4c/5x5"
  top: "inception_4c/5x5"
}
layer {
  name: "inception_4c/pool"
  type: "Pooling"
  bottom: "inception_4b/output"
  top: "inception_4c/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4c/pool_proj"
  type: "Convolution"
  bottom: "inception_4c/pool"
  top: "inception_4c/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4c/pool_proj"
  top: "inception_4c/pool_proj"
}
layer {
  name: "inception_4c/output"
  type: "Concat"
  bottom: "inception_4c/1x1"
  bottom: "inception_4c/3x3"
  bottom: "inception_4c/5x5"
  bottom: "inception_4c/pool_proj"
  top: "inception_4c/output"
}

layer {
  name: "inception_4d/1x1"
  type: "Convolution"
  bottom: "inception_4c/output"
  top: "inception_4d/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 112
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_1x1"
  type: "ReLU"
  bottom: "inception_4d/1x1"
  top: "inception_4d/1x1"
}
layer {
  name: "inception_4d/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4c/output"
  top: "inception_4d/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 144
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4d/3x3_reduce"
  top: "inception_4d/3x3_reduce"
}
layer {
  name: "inception_4d/3x3"
  type: "Convolution"
  bottom: "inception_4d/3x3_reduce"
  top: "inception_4d/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 288
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_3x3"
  type: "ReLU"
  bottom: "inception_4d/3x3"
  top: "inception_4d/3x3"
}
layer {
  name: "inception_4d/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4c/output"
  top: "inception_4d/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4d/5x5_reduce"
  top: "inception_4d/5x5_reduce"
}
layer {
  name: "inception_4d/5x5"
  type: "Convolution"
  bottom: "inception_4d/5x5_reduce"
  top: "inception_4d/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_5x5"
  type: "ReLU"
  bottom: "inception_4d/5x5"
  top: "inception_4d/5x5"
}
layer {
  name: "inception_4d/pool"
  type: "Pooling"
  bottom: "inception_4c/output"
  top: "inception_4d/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4d/pool_proj"
  type: "Convolution"
  bottom: "inception_4d/pool"
  top: "inception_4d/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4d/pool_proj"
  top: "inception_4d/pool_proj"
}
layer {
  name: "inception_4d/output"
  type: "Concat"
  bottom: "inception_4d/1x1"
  bottom: "inception_4d/3x3"
  bottom: "inception_4d/5x5"
  bottom: "inception_4d/pool_proj"
  top: "inception_4d/output"
}

layer {
  name: "inception_4e/1x1"
  type: "Convolution"
  bottom: "inception_4d/output"
  top: "inception_4e/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_1x1"
  type: "ReLU"
  bottom: "inception_4e/1x1"
  top: "inception_4e/1x1"
}
layer {
  name: "inception_4e/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4d/output"
  top: "inception_4e/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.09
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4e/3x3_reduce"
  top: "inception_4e/3x3_reduce"
}
layer {
  name: "inception_4e/3x3"
  type: "Convolution"
  bottom: "inception_4e/3x3_reduce"
  top: "inception_4e/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 320
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_3x3"
  type: "ReLU"
  bottom: "inception_4e/3x3"
  top: "inception_4e/3x3"
}
layer {
  name: "inception_4e/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4d/output"
  top: "inception_4e/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.2
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4e/5x5_reduce"
  top: "inception_4e/5x5_reduce"
}
layer {
  name: "inception_4e/5x5"
  type: "Convolution"
  bottom: "inception_4e/5x5_reduce"
  top: "inception_4e/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_5x5"
  type: "ReLU"
  bottom: "inception_4e/5x5"
  top: "inception_4e/5x5"
}
layer {
  name: "inception_4e/pool"
  type: "Pooling"
  bottom: "inception_4d/output"
  top: "inception_4e/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4e/pool_proj"
  type: "Convolution"
  bottom: "inception_4e/pool"
  top: "inception_4e/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4e/pool_proj"
  top: "inception_4e/pool_proj"
}
layer {
  name: "inception_4e/output"
  type: "Concat"
  bottom: "inception_4e/1x1"
  bottom: "inception_4e/3x3"
  bottom: "inception_4e/5x5"
  bottom: "inception_4e/pool_proj"
  top: "inception_4e/output"
}

layer {
  name: "inception_5a/1x1"
  type: "Convolution"
  bottom: "inception_4e/output"
  top: "inception_5a/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5a/relu_1x1"
  type: "ReLU"
  bottom: "inception_5a/1x1"
  top: "inception_5a/1x1"
}

layer {
  name: "inception_5a/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4e/output"
  top: "inception_5a/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.09
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5a/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_5a/3x3_reduce"
  top: "inception_5a/3x3_reduce"
}

layer {
  name: "inception_5a/3x3"
  type: "Convolution"
  bottom: "inception_5a/3x3_reduce"
  top: "inception_5a/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 320
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5a/relu_3x3"
  type: "ReLU"
  bottom: "inception_5a/3x3"
  top: "inception_5a/3x3"
}
layer {
  name: "inception_5a/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4e/output"
  top: "inception_5a/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.2
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5a/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_5a/5x5_reduce"
  top: "inception_5a/5x5_reduce"
}
layer {
  name: "inception_5a/5x5"
  type: "Convolution"
  bottom: "inception_5a/5x5_reduce"
  top: "inception_5a/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5a/relu_5x5"
  type: "ReLU"
  bottom: "inception_5a/5x5"
  top: "inception_5a/5x5"
}
layer {
  name: "inception_5a/pool"
  type: "Pooling"
  bottom: "inception_4e/output"
  top: "inception_5a/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_5a/pool_proj"
  type: "Convolution"
  bottom: "inception_5a/pool"
  top: "inception_5a/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5a/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_5a/pool_proj"
  top: "inception_5a/pool_proj"
}
layer {
  name: "inception_5a/output"
  type: "Concat"
  bottom: "inception_5a/1x1"
  bottom: "inception_5a/3x3"
  bottom: "inception_5a/5x5"
  bottom: "inception_5a/pool_proj"
  top: "inception_5a/output"
}

layer {
  name: "inception_5b/1x1"
  type: "Convolution"
  bottom: "inception_5a/output"
  top: "inception_5b/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5b/relu_1x1"
  type: "ReLU"
  bottom: "inception_5b/1x1"
  top: "inception_5b/1x1"
}
layer {
  name: "inception_5b/3x3_reduce"
  type: "Convolution"
  bottom: "inception_5a/output"
  top: "inception_5b/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5b/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_5b/3x3_reduce"
  top: "inception_5b/3x3_reduce"
}
layer {
  name: "inception_5b/3x3"
  type: "Convolution"
  bottom: "inception_5b/3x3_reduce"
  top: "inception_5b/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5b/relu_3x3"
  type: "ReLU"
  bottom: "inception_5b/3x3"
  top: "inception_5b/3x3"
}
layer {
  name: "inception_5b/5x5_reduce"
  type: "Convolution"
  bottom: "inception_5a/output"
  top: "inception_5b/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5b/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_5b/5x5_reduce"
  top: "inception_5b/5x5_reduce"
}
layer {
  name: "inception_5b/5x5"
  type: "Convolution"
  bottom: "inception_5b/5x5_reduce"
  top: "inception_5b/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5b/relu_5x5"
  type: "ReLU"
  bottom: "inception_5b/5x5"
  top: "inception_5b/5x5"
}
layer {
  name: "inception_5b/pool"
  type: "Pooling"
  bottom: "inception_5a/output"
  top: "inception_5b/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_5b/pool_proj"
  type: "Convolution"
  bottom: "inception_5b/pool"
  top: "inception_5b/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.1
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_5b/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_5b/pool_proj"
  top: "inception_5b/pool_proj"
}
layer {
  name: "inception_5b/output"
  type: "Concat"
  bottom: "inception_5b/1x1"
  bottom: "inception_5b/3x3"
  bottom: "inception_5b/5x5"
  bottom: "inception_5b/pool_proj"
  top: "inception_5b/output"
}
layer {
  name: "pool5/drop_s1"
  type: "Dropout"
  bottom: "inception_5b/output"
  top: "pool5/drop_s1"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "cvg/classifier"
  type: "Convolution"
  bottom: "pool5/drop_s1"
  top: "cvg/classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.
    }
  }
}
layer {
  name: "coverage/sig"
  type: "Sigmoid"
  bottom: "cvg/classifier"
  top: "coverage"
}
layer {
  name: "bbox/regressor"
  type: "Convolution"
  bottom: "pool5/drop_s1"
  top: "bboxes"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.
    }
  }
}

######################################################################
# End of convolutional network
######################################################################

# Convert bboxes
layer {
  name: "bbox_mask"
  type: "Eltwise"
  bottom: "bboxes"
  bottom: "coverage-block"
  top: "bboxes-masked"
  eltwise_param {
    operation: PROD
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "bbox-norm"
  type: "Eltwise"
  bottom: "bboxes-masked"
  bottom: "size-block"
  top: "bboxes-masked-norm"
  eltwise_param {
    operation: PROD
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "bbox-obj-norm"
  type: "Eltwise"
  bottom: "bboxes-masked-norm"
  bottom: "obj-block"
  top: "bboxes-obj-masked-norm"
  eltwise_param {
    operation: PROD
  }
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}

# Loss layers
layer {
  name: "bbox_loss"
  type: "L1Loss"
  bottom: "bboxes-obj-masked-norm"
  bottom: "bbox-obj-label-norm"
  top: "loss_bbox"
  loss_weight: 2
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}
layer {
  name: "coverage_loss"
  type: "EuclideanLoss"
  bottom: "coverage"
  bottom: "coverage-label"
  top: "loss_coverage"
  include { phase: TRAIN }
  include { phase: TEST stage: "val" }
}

# Cluster bboxes
layer {
    type: 'Python'
    name: 'cluster'
    bottom: 'coverage'
    bottom: 'bboxes'
    top: 'bbox-list-class0'
    top: 'bbox-list-class1'
    python_param {
        module: 'caffe.layers.detectnet.clustering'
        layer: 'ClusterDetections'
        param_str : '640, 640, 16, 0.6, 2, 0.02, 22, 2'
    }
    include: { phase: TEST }
}

# Calculate mean average precision
layer {
  type: 'Python'
  name: 'cluster_gt'
  bottom: 'coverage-label'
  bottom: 'bbox-label'
  top: 'bbox-list-label-class0'
  top: 'bbox-list-label-class1'
  python_param {
      module: 'caffe.layers.detectnet.clustering'
      layer: 'ClusterGroundtruth'
      param_str : '640, 640, 16, 2'
  }
  include: { phase: TEST stage: "val" }
}
layer {
    type: 'Python'
    name: 'score-class0'
    bottom: 'bbox-list-label-class0'
    bottom: 'bbox-list-class0'
    top: 'bbox-list-scored-class0'
    python_param {
        module: 'caffe.layers.detectnet.mean_ap'
        layer: 'ScoreDetections'
    }
    include: { phase: TEST stage: "val" }
}
layer {
    type: 'Python'
    name: 'mAP-class0'
    bottom: 'bbox-list-scored-class0'
    top: 'mAP-class0'
    top: 'precision-class0'
    top: 'recall-class0'
    python_param {
        module: 'caffe.layers.detectnet.mean_ap'
        layer: 'mAP'
        param_str : '640, 640, 16'
    }
    include: { phase: TEST stage: "val" }
}
layer {
    type: 'Python'
    name: 'score-class1'
    bottom: 'bbox-list-label-class1'
    bottom: 'bbox-list-class1'
    top: 'bbox-list-scored-class1'
    python_param {
        module: 'caffe.layers.detectnet.mean_ap'
        layer: 'ScoreDetections'
    }
    include: { phase: TEST stage: "val" }
}
layer {
    type: 'Python'
    name: 'mAP-class1'
    bottom: 'bbox-list-scored-class1'
    top: 'mAP-class1'
    top: 'precision-class1'
    top: 'recall-class1'
    python_param {
        module: 'caffe.layers.detectnet.mean_ap'
        layer: 'mAP'
        param_str : '640, 640, 16'
    }
    include: { phase: TEST stage: "val" }
}

What am I doing wrong?

ontheway16 commented 7 years ago

I made a diff and found the following:

layer {
  name: "cvg/classifier"
  type: "Convolution"
  bottom: "pool5/drop_s1"
  top: "cvg/classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    kernel_size: 1
    weight_filler {
      type: "xavier"
      std: 0.03
    }
    bias_filler {
      type: "constant"
      value: 0.
    }
  }
}

Above, "num_output: 1" is actually '2', in original two-class prototxt. I couldn't see any other problems.

antoniodourado commented 7 years ago

Nice!!! It was the problem. It's training the two-class model right now and I'll post the feedback here soon.

antoniodourado commented 7 years ago

Does it train for on class then for another one? I'm on the 2nd epoch and only class 1 is really training while class 0 is not. I set 30 epochs, maybe will it split 15/15?

captura de tela de 2017-05-31 14 59 32

ontheway16 commented 7 years ago

As far as I know, it trains for both at the same time. But be prepared to see lower mAP levels than single class trainings. Thats why I said, first get higher map levels for each class. Let it run for a while then then decide whether more data is necessary. That's what I am doing.

mayankmahajan21 commented 5 years ago

It looks like you have to change the layer name as you are training a custom data set. I had the same problem and changing the name of few inception layer worked for me.

vxgu86 commented 4 years ago

@mayankmahajan21 could you share some idea? I am having this very similar problem

mayankmahajan21 commented 4 years ago

@vxgu86 What is your problem? can you elaborate?

ontheway16 commented 4 years ago

It looks like you have to change the layer name as you are training a custom data set. I had the same problem and changing the name of few inception layer worked for me.

Hi, can you further describe what you exactly did? Or may be sharing that network part may help, since I don't understand how exactly the changes made. Thanks!

mayankmahajan21 commented 4 years ago

@ontheway16 visit the following link : https://www.coria.com/insights/blog/computer-vision/training-a-custom-mutliclass-object-detection-model