git clone https://github.com/xiangweizeng/darknet2ncnn.git

cd darknet2ncnngit submodule initgit submodule update

cd darknet2make -j8rm libdarknet.so

# workspace darknet2ncnncd ncnnmkdir buildcd buildcmake ..make -j8make installcd ../../

# workspace darknet2ncnnmake -j8

# workspace darknet2ncnnmake cifar./darknet2ncnn data/cifar.cfg  data/cifar.backup example/zoo/cifar.param  example/zoo/cifar.bin layer     filters    size              input                output    0 conv    128  3 x 3 / 1    28 x  28 x   3   ->    28 x  28 x 128  0.005 BFLOPs    1 conv    128  3 x 3 / 1    28 x  28 x 128   ->    28 x  28 x 128  0.231 BFLOPs...   13 dropout       p = 0.50               25088  ->  25088   14 conv     10  1 x 1 / 1     7 x   7 x 512   ->     7 x   7 x  10  0.001 BFLOPs   15 avg                        7 x   7 x  10   ->    10   16 softmax                                          10Loading weights from data/cifar.backup...Done!./convert_verify data/cifar.cfg  data/cifar.backup example/zoo/cifar.param  example/zoo/cifar.bin  example/data/21263_ship.pnglayer     filters    size              input                output    0 conv    128  3 x 3 / 1    28 x  28 x   3   ->    28 x  28 x 128  0.005 BFLOPs    1 conv    128  3 x 3 / 1    28 x  28 x 128   ->    28 x  28 x 128  0.231 BFLOPs...   13 dropout       p = 0.50               25088  ->  25088   14 conv     10  1 x 1 / 1     7 x   7 x 512   ->     7 x   7 x  10  0.001 BFLOPs   15 avg                        7 x   7 x  10   ->    10   16 softmax                                          10Loading weights from data/cifar.backup...Done!Start run all operation:conv_0 : weights diff : 0.000000conv_0_batch_norm : slope diff : 0.000000conv_0_batch_norm : mean diff : 0.000000conv_0_batch_norm : variance diff : 0.000000conv_0_batch_norm : biases diff : 0.000000Layer: 0, Blob : conv_0_activation, Total Diff 595.703918 Avg Diff: 0.005936...Layer: 14, Blob : conv_14_activation, Total Diff 35.058342 Avg Diff: 0.071548Layer: 15, Blob : gloabl_avg_pool_15, Total Diff 0.235242 Avg Diff: 0.023524Layer: 16, Blob : softmax_16, Total Diff 0.000001 Avg Diff: 0.000000

 make yolov3-tiny.net ./darknet2ncnn data/yolov3-tiny.cfg  data/yolov3-tiny.weights example/zoo/yolov3-tiny.param  example/zoo/yolov3-tiny.bin layer     filters    size              input                output    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16  0.150 BFLOPs...   22 conv    255  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 255  0.088 BFLOPs   23 yoloLoading weights from data/yolov3-tiny.weights...Done!./convert_verify data/yolov3-tiny.cfg  data/yolov3-tiny.weights example/zoo/yolov3-tiny.param  example/zoo/yolov3-tiny.bin example/data/dog.jpglayer     filters    size              input                output    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16  0.150 BFLOPs    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16...   20 route  19 8   21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256  1.196 BFLOPs   22 conv    255  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 255  0.088 BFLOPs   23 yoloLoading weights from data/yolov3-tiny.weights...Done!Start run all operation:conv_0 : weights diff : 0.000000conv_0_batch_norm : slope diff : 0.000000conv_0_batch_norm : mean diff : 0.000000conv_0_batch_norm : variance diff : 0.000000conv_0_batch_norm : biases diff : 0.000000...conv_22 : weights diff : 0.000000conv_22 : biases diff : 0.000000Layer: 22, Blob : conv_22_activation, Total Diff 29411.240234 Avg Diff: 0.170619

# workspace darknet2ncnncd examplemake -j2

# workspace examplemake cifar.cifar./classifier zoo/cifar.param  zoo/cifar.bin  data/32516_dog.png data/cifar_lable.txt4    deer                             = 0.2631036    frog                             = 0.2242745    dog                              = 0.1913603    cat                              = 0.1801642    bird                             = 0.094251

# workspace example make yolov3-tiny.coco ./yolo zoo/yolov3-tiny.param  zoo/yolov3-tiny.bin  data/dog.jpg  data/coco.names3  [car             ] = 0.64929 at 252.10 92.13 114.88 x 52.982  [bicycle         ] = 0.60786 at 111.18 134.81 201.40 x 160.0117 [dog             ] = 0.56338 at 69.91 152.89 130.30 x 179.048  [truck           ] = 0.54883 at 288.70 103.80 47.98 x 34.173  [car             ] = 0.28332 at 274.47 100.36 48.90 x 35.03

# workspace darknet2ncnncd benchmarkmake 

firefly@firefly:~/project/darknet2ncnn/benchmark$ ./benchdarknet 10  2 &[1] 4556loop_count = 10num_threads = 2powersave = 0firefly@firefly:~/project/darknet2ncnn/benchmark$ taskset -pc 4,5 4556pid 4556's current affinity list: 0-5pid 4556's new affinity list: 4,5                    cifar  min =   85.09  max =   89.15  avg =   85.81         alexnet  min =  218.38  max =  220.96  avg =  218.88         darknet  min =   88.38  max =   88.95  avg =   88.63       darknet19  min =  330.55  max =  337.12  avg =  333.64       darknet53  min =  874.69  max =  920.99  avg =  897.19     densenet201  min =  678.99  max =  684.97  avg =  681.38      extraction  min =  332.78  max =  340.54  avg =  334.98        resnet18  min =  238.93  max =  245.66  avg =  240.32        resnet34  min =  398.92  max =  404.93  avg =  402.18        resnet50  min =  545.39  max =  558.67  avg =  551.90       resnet101  min =  948.88  max =  960.51  avg =  952.99       resnet152  min = 1350.78  max = 1373.51  avg = 1363.40       resnext50  min =  660.55  max =  698.07  avg =  669.49resnext101-32x4d  min = 1219.80  max = 1232.07  avg = 1227.58resnext152-32x4d  min = 1788.03  max = 1798.79  avg = 1795.48          vgg-16  min =  883.33  max =  903.98  avg =  895.03     yolov1-tiny  min =  222.40  max =  227.51  avg =  224.67     yolov2-tiny  min =  250.54  max =  259.84  avg =  252.38     yolov3-tiny  min =  240.80  max =  249.98  avg =  245.08

firefly@firefly:~/project/darknet2ncnn/benchmark$ ./benchdarknet 10  4 &[1] 4663 loop_count = 10num_threads = 4powersave = 0firefly@firefly:~/project/darknet2ncnn/benchmark$ taskset -pc 0-3 4663pid 4663's current affinity list: 0-5pid 4663's new affinity list: 0-3                   cifar  min =   96.51  max =  108.22  avg =  100.60         alexnet  min =  411.38  max =  432.00  avg =  420.11         darknet  min =  101.89  max =  119.73  avg =  106.46       darknet19  min =  421.46  max =  453.59  avg =  433.74       darknet53  min = 1375.30  max = 1492.79  avg = 1406.82     densenet201  min = 1154.26  max = 1343.53  avg = 1218.28      extraction  min =  399.31  max =  460.01  avg =  428.17        resnet18  min =  317.70  max =  376.89  avg =  338.93        resnet34  min =  567.30  max =  604.44  avg =  580.65        resnet50  min =  838.94  max =  978.21  avg =  925.14       resnet101  min = 1562.60  max = 1736.91  avg = 1642.27       resnet152  min = 2250.32  max = 2394.38  avg = 2311.42       resnext50  min =  993.34  max = 1210.04  avg = 1093.05resnext101-32x4d  min = 2207.74  max = 2366.66  avg = 2281.82resnext152-32x4d  min = 3139.89  max = 3372.58  avg = 3282.99          vgg-16  min = 1259.17  max = 1359.55  avg = 1300.04     yolov1-tiny  min =  272.31  max =  330.71  avg =  295.98     yolov2-tiny  min =  314.25  max =  352.12  avg =  329.02     yolov3-tiny  min =  300.28  max =  349.13  avg =  322.54