How to convert the weights to caffemodel about caffe-yolov2

[Serge3006]

@zzh721 it is preferable to compile this version of caffe with the reorg layers implemented. However, the reorganize layer doesn't have weights so, in theory, you wouldn't have to have problems to make the transformation of the weights.

[Serge3006]

By the way I have used the following prototxt file:

PD: The weights can be download from:
https://pjreddie.com/darknet/yolov2/

The weights I used were the YOLOv2 608x608 COCO

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
}
}

[wuxianli930316]

hello,i want to know to compile this version of caffe with the reorg layers implemented.