# darknet2ncnn **Repository Path**: damone/darknet2ncnn ## Basic Information - **Project Name**: darknet2ncnn - **Description**: darknet2ncnn将darknet 模型转换为ncnn模型，实现darknet网络模型在移动端的快速部署 - **Primary Language**: C++ - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 25 - **Forks**: 16 - **Created**: 2018-11-08 - **Last Updated**: 2025-04-21 ## Categories & Tags **Categories**: ai **Tags**: None ## README # darknet2ncnn # 简介 darknet2ncnn将darknet 模型转换为ncnn模型，实现darknet网络模型在移动端的快速部署 [码云](https://gitee.com/damone/darknet2ncnn) : https://gitee.com/damone/darknet2ncnn 1. 除 local/xor conv, rnn, lstm, gru, crnn及iseg外，均提供支持 1. 自定义添加了所有ncnn不直接支持的activation操作，实现位于层DarknetActivation 1. 自定义添加了shortcut层的实现，实现位于层DarknetShortCut 1. 自定义添加了yolo层及detection层的实现，支持YOLOV1及YOLOV3 1. 提供了转换后的模型校验工具，convert_verify,支持检验每一层网络的计算输出，支持卷积层参数检查，方便快速定位模型转换中出现的问题 # 安装及使用 1. Install opencv-dev, gcc, g++, make, cmake 2. 下载源码 ```sh git clone https://github.com/xiangweizeng/darknet2ncnn.git ``` 3. 初始化 submodule ```sh cd darknet2ncnn git submodule init git submodule update ``` 4. 构建 darknet ```sh cd darknet2 make -j8 rm libdarknet.so ``` 5. 构建 ncnn ```sh # workspace darknet2ncnn cd ncnn mkdir build cd build cmake .. make -j8 make install cd ../../ ``` 6. 构建 darknet2ncnn , convert_verify and libdarknet2ncnn.a ```sh # workspace darknet2ncnn make -j8 ``` 7. 模型转换及验证 - Cifar ```sh # 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 . . . 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 . . . 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 . . . 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 ```sh 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 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 . . . 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 ``` 8. 构建 example ```sh # workspace darknet2ncnn cd example make -j2 ``` 10. 运行 classifier ```sh # 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 ``` 11. 运行 Yolo - Run YoloV3-tiny ```sh # 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: ![image/](image/yolov3-tiny-ncnn.png) DARKNET: ![image/](image/yolov3-tiny-darknet.jpg) 12. 构建 benchmark ```sh # workspace darknet2ncnn cd benchmark make ``` 13. 运行 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 ``` # 支持的网络模型(Zoo) [Zoo（百度云）：](https://pan.baidu.com/s/1BgqL8p1yB4gRPrxAK73omw):https://pan.baidu.com/s/1BgqL8p1yB4gRPrxAK73omw ## Cifar 1. cifar ## ImageNet 1. alexnet 2. darknet 3. darknet19 4. darknet53 5. densenet201 6. extraction 7. resnet18 8. resnet34 9. resnet50 10. resnet101 11. resnet152 12. resnext50 13. resnext101-32x4d 14. resnext152-32x4d 15. vgg-16 ## YOLO 1. yolov1-tiny 2. yolov2-tiny 3. yolov2 4. yolov3-tiny 5. yolov3 6. yolov3-spp # 性能评估 时间单位： 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