How to convert the weights to caffemodel?

[yanzi6969]

Thanks for your share.I meet a problem when I try to convert the weights to caffemodel.
python convert_weights_to_caffemodel.py yolo.prototxt yolo.weights yolo.caffemodel
the yolo.prototxt is provided by you,and the yolo.caffemodel(yolov2) is downloaded from https://github.com/AlexeyAB/darknet ,when I run the program,I got the error:
conv20(conv)
bn20(batchnorm)
scale20(scale)
conv21(conv)
Traceback (most recent call last):
File "convert_weights_to_caffemodel.py", line 70, in
net.params[pr][0].data[...] = np.reshape(netWeights[count:count + weightSize], dims)
File "/home/zcy/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 232, in reshape
return _wrapfunc(a, 'reshape', newshape, order=order)
File "/home/zcy/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 57, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
ValueError: cannot reshape array of size 12265129 into shape (1024,3072,3,3)
I dont know why this happened?

[JingliangGao]

Luckly，My workmate and I found the reason. The .weights you have download is not right, by the way, which means the .weights you have download can not match the .prototxt . You can download the old yolov2's weight here——https://drive.google.com/drive/folders/0B1tW_VtY7onidEwyQ2FtQVplWEU

[Serge3006]

Hi, I have the same issue when I try to convert the weights using the prototxt provided in indoor/convert/ and the weights provided in the yolo v2 webpage https://pjreddie.com/darknet/yolov2/. When I run the script the following error shows up:

Traceback (most recent call last):
File "convert_weights_to_caffemodel.py", line 68, in
net.params[pr][0].data[...] = np.reshape(netWeights[count:count + weightSize], dims)
File "/usr/lib/python2.7/dist-packages/numpy/core/fromnumeric.py", line 225, in reshape
return reshape(newshape, order=order)
ValueError: total size of new array must be unchanged

Can someone when help me!

@JingliangGao I've tried with your weights but still have the same error.

[djiayong5]

The yolo.protext is a wrong, please use the following text and instead of yolo.protext:
name: "YOLONET"
layer {
name: "data"
type: "Input"
top: "data"
top: "label"
input_param { shape: { dim: 1 dim: 3 dim: 416 dim: 416 }
shape: { dim: 1 dim: 1 dim: 30 dim: 5 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 32
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn1"
type: "BatchNorm"
bottom: "conv1"
top: "bn1"
}
layer {
name: "scale1"
type: "Scale"
bottom: "bn1"
top: "scale1"
scale_param {
bias_term: true
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "scale1"
top: "scale1"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "scale1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer{
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn2"
type: "BatchNorm"
bottom: "conv2"
top: "bn2"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale2"
type: "Scale"
bottom: "bn2"
top: "scale2"
scale_param {
bias_term: true
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "scale2"
top: "scale2"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "scale2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn3"
type: "BatchNorm"
bottom: "conv3"
top: "bn3"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale3"
type: "Scale"
bottom: "bn3"
top: "scale3"
scale_param {
bias_term: true
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "scale3"
top: "scale3"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv4"
type: "Convolution"
bottom: "scale3"
top: "conv4"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0 #??
stride: 1
bias_term: false
}
}
layer {
name: "bn4"
type: "BatchNorm"
bottom: "conv4"
top: "bn4"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale4"
type: "Scale"
bottom: "bn4"
top: "scale4"
scale_param {
bias_term: true
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "scale4"
top: "scale4"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv5"
type: "Convolution"
bottom: "scale4"
top: "conv5"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn5"
type: "BatchNorm"
bottom: "conv5"
top: "bn5"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale5"
type: "Scale"
bottom: "bn5"
top: "scale5"
scale_param {
bias_term: true
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "scale5"
top: "scale5"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool5"
type: "Pooling"
bottom: "scale5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv6"
type: "Convolution"
bottom: "pool5"
top: "conv6"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn6"
type: "BatchNorm"
bottom: "conv6"
top: "bn6"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale6"
type: "Scale"
bottom: "bn6"
top: "scale6"
scale_param {
bias_term: true
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "scale6"
top: "scale6"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv7"
type: "Convolution"
bottom: "scale6"
top: "conv7"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn7"
type: "BatchNorm"
bottom: "conv7"
top: "bn7"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale7"
type: "Scale"
bottom: "bn7"
top: "scale7"
scale_param {
bias_term: true
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "scale7"
top: "scale7"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv8"
type: "Convolution"
bottom: "scale7"
top: "conv8"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn8"
type: "BatchNorm"
bottom: "conv8"
top: "bn8"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale8"
type: "Scale"
bottom: "bn8"
top: "scale8"
scale_param {
bias_term: true
}
}
layer {
name: "relu8"
type: "ReLU"
bottom: "scale8"
top: "scale8"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool8"
type: "Pooling"
bottom: "scale8"
top: "pool8"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv9"
type: "Convolution"
bottom: "pool8"
top: "conv9"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn9"
type: "BatchNorm"
bottom: "conv9"
top: "bn9"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale9"
type: "Scale"
bottom: "bn9"
top: "scale9"
scale_param {
bias_term: true
}
}
layer {
name: "relu9"
type: "ReLU"
bottom: "scale9"
top: "scale9"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv10"
type: "Convolution"
bottom: "scale9"
top: "conv10"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn10"
type: "BatchNorm"
bottom: "conv10"
top: "bn10"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale10"
type: "Scale"
bottom: "bn10"
top: "scale10"
scale_param {
bias_term: true
}
}
layer {
name: "relu10"
type: "ReLU"
bottom: "scale10"
top: "scale10"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv11"
type: "Convolution"
bottom: "scale10"
top: "conv11"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn11"
type: "BatchNorm"
bottom: "conv11"
top: "bn11"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale11"
type: "Scale"
bottom: "bn11"
top: "scale11"
scale_param {
bias_term: true
}
}
layer {
name: "relu11"
type: "ReLU"
bottom: "scale11"
top: "scale11"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv12"
type: "Convolution"
bottom: "scale11"
top: "conv12"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn12"
type: "BatchNorm"
bottom: "conv12"
top: "bn12"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale12"
type: "Scale"
bottom: "bn12"
top: "scale12"
scale_param {
bias_term: true
}
}
layer {
name: "relu12"
type: "ReLU"
bottom: "scale12"
top: "scale12"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv13"
type: "Convolution"
bottom: "scale12"
top: "conv13"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn13"
type: "BatchNorm"
bottom: "conv13"
top: "bn13"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale13"
type: "Scale"
bottom: "bn13"
top: "scale13"
scale_param {
bias_term: true
}
}
layer {
name: "relu13"
type: "ReLU"
bottom: "scale13"
top: "scale13"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool13"
type: "Pooling"
bottom: "scale13"
top: "pool13"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv14"
type: "Convolution"
bottom: "pool13"
top: "conv14"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn14"
type: "BatchNorm"
bottom: "conv14"
top: "bn14"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale14"
type: "Scale"
bottom: "bn14"
top: "scale14"
scale_param {
bias_term: true
}
}
layer {
name: "relu14"
type: "ReLU"
bottom: "scale14"
top: "scale14"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv15"
type: "Convolution"
bottom: "scale14"
top: "conv15"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn15"
type: "BatchNorm"
bottom: "conv15"
top: "bn15"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale15"
type: "Scale"
bottom: "bn15"
top: "scale15"
scale_param {
bias_term: true
}
}
layer {
name: "relu15"
type: "ReLU"
bottom: "scale15"
top: "scale15"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv16"
type: "Convolution"
bottom: "scale15"
top: "conv16"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn16"
type: "BatchNorm"
bottom: "conv16"
top: "bn16"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale16"
type: "Scale"
bottom: "bn16"
top: "scale16"
scale_param {
bias_term: true
}
}
layer {
name: "relu16"
type: "ReLU"
bottom: "scale16"
top: "scale16"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv17"
type: "Convolution"
bottom: "scale16"
top: "conv17"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn17"
type: "BatchNorm"
bottom: "conv17"
top: "bn17"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale17"
type: "Scale"
bottom: "bn17"
top: "scale17"
scale_param {
bias_term: true
}
}
layer {
name: "relu17"
type: "ReLU"
bottom: "scale17"
top: "scale17"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv18"
type: "Convolution"
bottom: "scale17"
top: "conv18"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn18"
type: "BatchNorm"
bottom: "conv18"
top: "bn18"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale18"
type: "Scale"
bottom: "bn18"
top: "scale18"
scale_param {
bias_term: true
}
}
layer {
name: "relu18"
type: "ReLU"
bottom: "scale18"
top: "scale18"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv19"
type: "Convolution"
bottom: "scale18"
top: "conv19"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn19"
type: "BatchNorm"
bottom: "conv19"
top: "bn19"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale19"
type: "Scale"
bottom: "bn19"
top: "scale19"
scale_param {
bias_term: true
}
}
layer {
name: "relu19"
type: "ReLU"
bottom: "scale19"
top: "scale19"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv20"
type: "Convolution"
bottom: "scale19"
top: "conv20"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}

}
layer {
name: "bn20"
type: "BatchNorm"
bottom: "conv20"
top: "bn20"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale20"
type: "Scale"
bottom: "bn20"
top: "scale20"
scale_param {
bias_term: true
}
}
layer {
name: "relu20"
type: "ReLU"
bottom: "scale20"
top: "scale20"
relu_param {
negative_slope: 0.1
}
}

layer {
name: "concat1"
type: "Concat"
bottom: "scale13"
top: "concat1"
}

layer{
name: "conv21"
type: "Convolution"
bottom: "concat1"
top: "conv21"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}

}
layer {
name: "bn21"
type: "BatchNorm"
bottom: "conv21"
top: "bn21"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale21"
type: "Scale"
bottom: "bn21"
top: "scale21"
scale_param {
bias_term: true
}
}
layer {
name: "relu21"
type: "ReLU"
bottom: "scale21"
top: "scale21"
relu_param {
negative_slope: 0.1
}
}

layer {
name: "reorg1"
type: "Reorg"
bottom: "scale21"
top: "reorg1"
reorg_param {
stride: 2
}
}

layer {
name: "concat2"
type: "Concat"
bottom: "reorg1"
bottom: "scale20"
top: "concat2"
}

layer{
name: "conv22"
type: "Convolution"
bottom: "concat2"
top: "conv22"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn22"
type: "BatchNorm"
bottom: "conv22"
top: "bn22"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale22"
type: "Scale"
bottom: "bn22"
top: "scale22"
scale_param {
bias_term: true
}
}
layer {
name: "relu22"
type: "ReLU"
bottom: "scale22"
top: "scale22"
relu_param{
negative_slope: 0.1
}
}

layer {
name: "conv23"
type: "Convolution"
bottom: "scale22"
top: "conv23"
convolution_param {
num_output: 425
kernel_size: 1
pad: 0
stride: 1
}
}
layer {
name: "region1"
type: "RegionLoss"
bottom: "conv23"
bottom: "label"
top: "region1"
region_loss_param {
side: 13
num_class: 80
coords: 4
num: 5
}
}

[SimonPrPr]

@djiayong5 Could you please tell me which weights is attached to the prototxt you gave?
Is the weights from https://pjreddie.com/darknet/yolov2/ ?

[Serge3006]

The prototxt file I have use is the following:

PD: I have used the YOLO v2 608x608 coco weights provided in the preddie page.
https://pjreddie.com/darknet/yolov2/

name: "YOLONET"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 608
dim: 608
}

layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 32
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn1"
type: "BatchNorm"
bottom: "conv1"
top: "bn1"
}
layer {
name: "scale1"
type: "Scale"
bottom: "bn1"
top: "scale1"
scale_param {
bias_term: true
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "scale1"
top: "scale1"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "scale1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer{
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn2"
type: "BatchNorm"
bottom: "conv2"
top: "bn2"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale2"
type: "Scale"
bottom: "bn2"
top: "scale2"
scale_param {
bias_term: true
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "scale2"
top: "scale2"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "scale2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn3"
type: "BatchNorm"
bottom: "conv3"
top: "bn3"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale3"
type: "Scale"
bottom: "bn3"
top: "scale3"
scale_param {
bias_term: true
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "scale3"
top: "scale3"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv4"
type: "Convolution"
bottom: "scale3"
top: "conv4"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn4"
type: "BatchNorm"
bottom: "conv4"
top: "bn4"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale4"
type: "Scale"
bottom: "bn4"
top: "scale4"
scale_param {
bias_term: true
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "scale4"
top: "scale4"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv5"
type: "Convolution"
bottom: "scale4"
top: "conv5"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn5"
type: "BatchNorm"
bottom: "conv5"
top: "bn5"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale5"
type: "Scale"
bottom: "bn5"
top: "scale5"
scale_param {
bias_term: true
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "scale5"
top: "scale5"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool5"
type: "Pooling"
bottom: "scale5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv6"
type: "Convolution"
bottom: "pool5"
top: "conv6"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn6"
type: "BatchNorm"
bottom: "conv6"
top: "bn6"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale6"
type: "Scale"
bottom: "bn6"
top: "scale6"
scale_param {
bias_term: true
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "scale6"
top: "scale6"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv7"
type: "Convolution"
bottom: "scale6"
top: "conv7"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn7"
type: "BatchNorm"
bottom: "conv7"
top: "bn7"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale7"
type: "Scale"
bottom: "bn7"
top: "scale7"
scale_param {
bias_term: true
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "scale7"
top: "scale7"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv8"
type: "Convolution"
bottom: "scale7"
top: "conv8"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn8"
type: "BatchNorm"
bottom: "conv8"
top: "bn8"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale8"
type: "Scale"
bottom: "bn8"
top: "scale8"
scale_param {
bias_term: true
}
}
layer {
name: "relu8"
type: "ReLU"
bottom: "scale8"
top: "scale8"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool8"
type: "Pooling"
bottom: "scale8"
top: "pool8"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv9"
type: "Convolution"
bottom: "pool8"
top: "conv9"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn9"
type: "BatchNorm"
bottom: "conv9"
top: "bn9"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale9"
type: "Scale"
bottom: "bn9"
top: "scale9"
scale_param {
bias_term: true
}
}
layer {
name: "relu9"
type: "ReLU"
bottom: "scale9"
top: "scale9"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv10"
type: "Convolution"
bottom: "scale9"
top: "conv10"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn10"
type: "BatchNorm"
bottom: "conv10"
top: "bn10"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale10"
type: "Scale"
bottom: "bn10"
top: "scale10"
scale_param {
bias_term: true
}
}
layer {
name: "relu10"
type: "ReLU"
bottom: "scale10"
top: "scale10"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv11"
type: "Convolution"
bottom: "scale10"
top: "conv11"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn11"
type: "BatchNorm"
bottom: "conv11"
top: "bn11"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale11"
type: "Scale"
bottom: "bn11"
top: "scale11"
scale_param {
bias_term: true
}
}
layer {
name: "relu11"
type: "ReLU"
bottom: "scale11"
top: "scale11"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv12"
type: "Convolution"
bottom: "scale11"
top: "conv12"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn12"
type: "BatchNorm"
bottom: "conv12"
top: "bn12"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale12"
type: "Scale"
bottom: "bn12"
top: "scale12"
scale_param {
bias_term: true
}
}
layer {
name: "relu12"
type: "ReLU"
bottom: "scale12"
top: "scale12"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv13"
type: "Convolution"
bottom: "scale12"
top: "conv13"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn13"
type: "BatchNorm"
bottom: "conv13"
top: "bn13"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale13"
type: "Scale"
bottom: "bn13"
top: "scale13"
scale_param {
bias_term: true
}
}
layer {
name: "relu13"
type: "ReLU"
bottom: "scale13"
top: "scale13"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool13"
type: "Pooling"
bottom: "scale13"
top: "pool13"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

layer{
name: "conv14"
type: "Convolution"
bottom: "pool13"
top: "conv14"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn14"
type: "BatchNorm"
bottom: "conv14"
top: "bn14"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale14"
type: "Scale"
bottom: "bn14"
top: "scale14"
scale_param {
bias_term: true
}
}
layer {
name: "relu14"
type: "ReLU"
bottom: "scale14"
top: "scale14"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv15"
type: "Convolution"
bottom: "scale14"
top: "conv15"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn15"
type: "BatchNorm"
bottom: "conv15"
top: "bn15"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale15"
type: "Scale"
bottom: "bn15"
top: "scale15"
scale_param {
bias_term: true
}
}
layer {
name: "relu15"
type: "ReLU"
bottom: "scale15"
top: "scale15"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv16"
type: "Convolution"
bottom: "scale15"
top: "conv16"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn16"
type: "BatchNorm"
bottom: "conv16"
top: "bn16"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale16"
type: "Scale"
bottom: "bn16"
top: "scale16"
scale_param {
bias_term: true
}
}
layer {
name: "relu16"
type: "ReLU"
bottom: "scale16"
top: "scale16"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv17"
type: "Convolution"
bottom: "scale16"
top: "conv17"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn17"
type: "BatchNorm"
bottom: "conv17"
top: "bn17"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale17"
type: "Scale"
bottom: "bn17"
top: "scale17"
scale_param {
bias_term: true
}
}
layer {
name: "relu17"
type: "ReLU"
bottom: "scale17"
top: "scale17"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv18"
type: "Convolution"
bottom: "scale17"
top: "conv18"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn18"
type: "BatchNorm"
bottom: "conv18"
top: "bn18"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale18"
type: "Scale"
bottom: "bn18"
top: "scale18"
scale_param {
bias_term: true
}
}
layer {
name: "relu18"
type: "ReLU"
bottom: "scale18"
top: "scale18"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv19"
type: "Convolution"
bottom: "scale18"
top: "conv19"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn19"
type: "BatchNorm"
bottom: "conv19"
top: "bn19"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale19"
type: "Scale"
bottom: "bn19"
top: "scale19"
scale_param {
bias_term: true
}
}
layer {
name: "relu19"
type: "ReLU"
bottom: "scale19"
top: "scale19"
relu_param{
negative_slope: 0.1
}
}

layer{
name: "conv20"
type: "Convolution"
bottom: "scale19"
top: "conv20"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}

}
layer {
name: "bn20"
type: "BatchNorm"
bottom: "conv20"
top: "bn20"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale20"
type: "Scale"
bottom: "bn20"
top: "scale20"
scale_param {
bias_term: true
}
}
layer {
name: "relu20"
type: "ReLU"
bottom: "scale20"
top: "scale20"
relu_param {
negative_slope: 0.1
}
}

layer {
name: "concat1"
type: "Concat"
bottom: "scale13"
top: "concat1"
}

layer {
name: "conv21"
type: "Convolution"
bottom: "concat1"
top: "conv21"
convolution_param {
num_output: 64
kernel_size: 1
stride: 1
pad: 0
bias_term: false
}
}
layer {
name: "bn21"
type: "BatchNorm"
bottom: "conv21"
top: "bn21"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}

layer {
name: "scale21"
type: "Scale"
bottom: "bn21"
top: "scale21"
scale_param {
bias_term: true
}
}
layer {
name: "relu21"
type: "ReLU"
bottom: "scale21"
top: "scale21"
relu_param{
negative_slope: 0.1
}
}

layer {
name: "reorg1"
type: "Reorg"
bottom: "scale21"
top: "reorg1"
reorg_param {
stride: 2
}
}

layer {
name: "concat2"
type: "Concat"
bottom: "reorg1"
bottom: "scale20"
top: "concat2"
}

layer{
name: "conv22"
type: "Convolution"
bottom: "concat2"
top: "conv22"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn22"
type: "BatchNorm"
bottom: "conv22"
top: "bn22"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale22"
type: "Scale"
bottom: "bn22"
top: "scale22"
scale_param {
bias_term: true
}
}
layer {
name: "relu22"
type: "ReLU"
bottom: "scale22"
top: "scale22"
relu_param{
negative_slope: 0.1
}
}

layer {
name: "conv23"
type: "Convolution"
bottom: "scale22"
top: "conv23"
convolution_param {
num_output: 425
kernel_size: 1
stride: 1
pad: 0
}
}

[mozpp]

yolo官网的cfg和这里的prototxt不同。
从yolo-voc.cfg文件可以看到，第25层为route层，逆向9层拿到第16层26 * 26 * 512的输出，并由第26层的reorg层把26 * 26 * 512 变形为13 * 13 * 2048，再有第27层的route层连接24层和26层的输出，堆叠为13 * 13 * 3072，由最后一个卷积核为3 * 3的卷积层进行跨通道的信息融合并把通道降维为1024。