Darknet2ncnn converts the darknet model to the ncnn model, enabling rapid deployment of the darknet network model on the mobile device.
Gitee : https://gitee.com/damone/darknet2ncnn
- Support network layers except local/xor conv, rnn, lstm, gru, crnn and iseg
- Added all activation operations not directly supported by ncnn, implemented in the layer DarknetActivation
- Added the implementation of the shortcut layer, implemented in the layer DarknetShortCut
- Added yolo layer and detection layer implementation, support YOLOV1 and YOLOV3
- Provides a converted model verification tool, convert_verify, which supports checking the calculation output of each layer of the network, supports convolutional layer parameter checking, and facilitates rapid positioning of problems in model conversion.
点击链接加入群聊【darknet2ncnn】：https://jq.qq.com/?_wv=1027&k=5Gou5zw
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Install opencv-dev, gcc, g++, make, cmake
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Download source
git clone https://github.com/xiangweizeng/darknet2ncnn.git- Init submodule
cd darknet2ncnn
git submodule init
git submodule update- build darknet
cd darknet
make -j8
rm libdarknet.so- build ncnn
# workspace darknet2ncnn
cd ncnn
mkdir build
cd build
cmake ..
make -j8
make install
cd ../../- Build darknet2ncnn , convert_verify and libdarknet2ncnn.a
# workspace darknet2ncnn
make -j8- Convert and verify
- Cifar
# workspace darknet2ncnn
make 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
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.
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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 10
Loading 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.png
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
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.
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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 10
Loading weights from data/cifar.backup...Done!
Start run all operation:
conv_0 : weights diff : 0.000000
conv_0_batch_norm : slope diff : 0.000000
conv_0_batch_norm : mean diff : 0.000000
conv_0_batch_norm : variance diff : 0.000000
conv_0_batch_norm : biases diff : 0.000000
Layer: 0, Blob : conv_0_activation, Total Diff 595.703918 Avg Diff: 0.005936
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.
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Layer: 14, Blob : conv_14_activation, Total Diff 35.058342 Avg Diff: 0.071548
Layer: 15, Blob : gloabl_avg_pool_15, Total Diff 0.235242 Avg Diff: 0.023524
Layer: 16, Blob : softmax_16, Total Diff 0.000001 Avg Diff: 0.000000
- Yolov3-tiny
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
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.
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22 conv 255 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BFLOPs
23 yolo
Loading 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.jpg
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
1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16
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.
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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 yolo
Loading weights from data/yolov3-tiny.weights...Done!
Start run all operation:
conv_0 : weights diff : 0.000000
conv_0_batch_norm : slope diff : 0.000000
conv_0_batch_norm : mean diff : 0.000000
conv_0_batch_norm : variance diff : 0.000000
conv_0_batch_norm : biases diff : 0.000000
.
.
.
conv_22 : weights diff : 0.000000
conv_22 : biases diff : 0.000000
Layer: 22, Blob : conv_22_activation, Total Diff 29411.240234 Avg Diff: 0.170619- Build example
# workspace darknet2ncnn
cd example
make -j2- Run classifier
# workspace example
make cifar.cifar
./classifier zoo/cifar.param zoo/cifar.bin data/32516_dog.png data/cifar_lable.txt
4 deer = 0.263103
6 frog = 0.224274
5 dog = 0.191360
3 cat = 0.180164
2 bird = 0.094251- Run Yolo
- Run YoloV3-tiny
# workspace example
make yolov3-tiny.coco
./yolo zoo/yolov3-tiny.param zoo/yolov3-tiny.bin data/dog.jpg data/coco.names
3 [car ] = 0.64929 at 252.10 92.13 114.88 x 52.98
2 [bicycle ] = 0.60786 at 111.18 134.81 201.40 x 160.01
17 [dog ] = 0.56338 at 69.91 152.89 130.30 x 179.04
8 [truck ] = 0.54883 at 288.70 103.80 47.98 x 34.17
3 [car ] = 0.28332 at 274.47 100.36 48.90 x 35.03- YoloV3-tiny figure
NCNN:
DARKNET:
- Build benchmark
# workspace darknet2ncnn
cd benchmark
make - Run benchmark
- Firefly RK3399 thread2
firefly@firefly:~/project/darknet2ncnn/benchmark$ ./benchdarknet 10 2 &
[1] 4556
loop_count = 10
num_threads = 2
powersave = 0
firefly@firefly:~/project/darknet2ncnn/benchmark$ taskset -pc 4,5 4556
pid 4556's current affinity list: 0-5
pid 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 = 669.49
resnext101-32x4d min = 1219.80 max = 1232.07 avg = 1227.58
resnext152-32x4d 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 RK3399 thread4
firefly@firefly:~/project/darknet2ncnn/benchmark$ ./benchdarknet 10 4 &
[1] 4663
loop_count = 10
num_threads = 4
powersave = 0
firefly@firefly:~/project/darknet2ncnn/benchmark$ taskset -pc 0-3 4663
pid 4663's current affinity list: 0-5
pid 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 = 1093.05
resnext101-32x4d min = 2207.74 max = 2366.66 avg = 2281.82
resnext152-32x4d 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
Zoo(Baidu Cloud):https://pan.baidu.com/s/1BgqL8p1yB4gRPrxAK73omw
- cifar
- alexnet
- darknet
- darknet19
- darknet53
- densenet201
- extraction
- resnet18
- resnet34
- resnet50
- resnet101
- resnet152
- resnext50
- resnext101-32x4d
- resnext152-32x4d
- vgg-16
- yolov1-tiny
- yolov2-tiny
- yolov2
- yolov3-tiny
- yolov3
- yolov3-spp
Time： ms
| Network | i7-7700K 4.20GHz 8thread | IMX6Q,Topeet 4thead | Firefly rk3399 2thread | Firefly rk3399 4thread |
|---|---|---|---|---|
| cifar | 62 | 302 | 85 | 100 |
| alexnet | 92 | 649 | 218 | 420 |
| darknet | 28 | 297 | 88 | 106 |
| darknet19 | 202 | 1218 | 333 | 433 |
| darknet53 | 683 | 3235 | 897 | 1406 |
| densenet201 | 218 | 2647 | 681 | 1218 |
| extraction | 244 | 1226 | 334 | 428 |
| resnet18 | 174 | 764 | 240 | 338 |
| resnet34 | 311 | 1408 | 402 | 580 |
| resnet50 | 276 | 2092 | 551 | 925 |
| resnet101 | 492 | 3758 | 952 | 1642 |
| resnet152 | 704 | 5500 | 1363 | 2311 |
| resnext50 | 169 | 2595 | 669 | 1093 |
| resnext101-32x4d | 296 | 5274 | 1227 | 2281 |
| resnext152-32x4d | 438 | 7818 | 1795 | 3282 |
| vgg-16 | 884 | 3597 | 895 | 1300 |
| yolov1-tiny | 98 | 843 | 224 | 295 |
| yolov2-tiny | 155 | 987 | 252 | 329 |
| yolov2 | 1846 | Out of memofy | Out of memofy | Out of memofy |
| yolov3-tiny | 159 | 951 | 245 | 322 |
| yolov3 | 5198 | Out of memofy | Out of memofy | Out of memofy |
| yolov3-spp | 5702 | Out of memofy | Out of memofy | Out of memofy |