配置YOLO2（ubuntu16.04+cuda8.0+opencv3.1.0）

要求已經安裝好了CUDA 8.0 以及OpenCV3.1.0

YOLO官方網站

配置darknet

git clone https://github.com/pjreddie/darknet 
cd darknet make 

如果沒有報錯輸入

./darknet  

得到輸出

./darknet <function>  

說明darknet配置成功

打開Makefile文件，將開頭幾行改為

GPU=1 CUDNN=1 OPENCV=1

接着查詢自己GPU的計算能力，查看Makefile是否包含，如下我的GPU計算能力為6.1,調整后為：

ARCH= -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_61,code=[sm_61,compute_61] \ -gencode arch=compute_52,code=[sm_52,compute_52]

之后重新編譯，就可以實現基於cuda和opencv的編譯

下載預訓練文件

wget https://pjreddie.com/media/files/yolo.weights 

測試

./darknet detect cfg/yolo.cfg yolo.weights data/dog.jpg  

我得到的結果是：

layer filters size input output 0 conv     32  3 x 3 / 1   608 x 608 x   3   ->   608 x 608 x  32
    1 max          2 x 2 / 2   608 x 608 x  32   ->   304 x 304 x  32
    2 conv     64  3 x 3 / 1   304 x 304 x  32   ->   304 x 304 x  64
    3 max          2 x 2 / 2   304 x 304 x  64   ->   152 x 152 x  64
    4 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128
    5 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64
    6 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128
    7 max          2 x 2 / 2   152 x 152 x 128   ->    76 x  76 x 128
    8 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256
    9 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128
   10 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256
   11 max          2 x 2 / 2    76 x  76 x 256   ->    38 x  38 x 256
   12 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512
   13 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256
   14 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512
   15 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256
   16 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512
   17 max          2 x 2 / 2    38 x  38 x 512   ->    19 x  19 x 512
   18 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024 19 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512
   20 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024 21 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512
   22 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024 23 conv   1024  3 x 3 / 1    19 x  19 x1024   ->    19 x  19 x1024 24 conv   1024  3 x 3 / 1    19 x  19 x1024   ->    19 x  19 x1024 25 route  16
   26 conv     64  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x  64
   27 reorg              / 2    38 x  38 x  64   ->    19 x  19 x 256
   28 route  27 24
   29 conv   1024  3 x 3 / 1    19 x  19 x1280   ->    19 x  19 x1024 30 conv    425  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 425
   31 detection mask_scale: Using default '1.000000' Loading weights from yolo.weights...Done! data/dog.jpg: Predicted in 0.070790 seconds. dog: 82% car: 26% truck: 65% bicycle: 85% init done opengl support available