ncnn 是一个为手机端极致优化的高性能神经网络前向计算框架。 ncnn 从设计之初深刻考虑手机端的部署和使用。 无第三方依赖，跨平台，手机端 cpu 的速度快于目前所有已知的开源框架。 基于 ncnn，开发者能够将深度学习算法轻松移植到手机端高效执行， 开发出人工智能 APP，将 AI 带到你的指尖。
ncnn 源码下载
代码语言：javascript复制git clone --recursive --depth 1 https://github.com/Tencent/ncnn.git
ncnn 编译
代码语言：javascript复制cd ncnn
mkdir build
cd build
cmake ..
开始编译
代码语言：javascript复制make -j6
编译完成后，会提示编译已完成
测试 ncnn
将测试使用的 benchncnn 复制到 ncnn/benchmark 文件夹内，直接运行测试
测试结果如下：
代码语言：javascript复制avaota@avaota-a1:~/ncnn/benchmark$ ./benchncnn
loop_count = 4
num_threads = 4
powersave = 2
gpu_device = -1
cooling_down = 1
squeezenet min = 16.49 max = 17.01 avg = 16.64
squeezenet_int8 min = 18.11 max = 18.26 avg = 18.17
mobilenet min = 22.10 max = 22.19 avg = 22.16
mobilenet_int8 min = 19.75 max = 19.96 avg = 19.81
mobilenet_v2 min = 21.52 max = 22.08 avg = 21.80
mobilenet_v3 min = 17.27 max = 17.48 avg = 17.40
shufflenet min = 12.48 max = 12.58 avg = 12.54
shufflenet_v2 min = 11.47 max = 11.81 avg = 11.58
mnasnet min = 17.57 max = 17.63 avg = 17.60
proxylessnasnet min = 23.15 max = 23.51 avg = 23.34
efficientnet_b0 min = 28.56 max = 28.88 avg = 28.68
efficientnetv2_b0 min = 33.63 max = 34.17 avg = 33.82
regnety_400m min = 28.87 max = 29.00 avg = 28.93
blazeface min = 4.11 max = 4.15 avg = 4.13
googlenet min = 60.61 max = 61.63 avg = 61.10
googlenet_int8 min = 59.67 max = 60.04 avg = 59.89
resnet18 min = 46.53 max = 47.01 avg = 46.74
resnet18_int8 min = 43.07 max = 43.77 avg = 43.53
alexnet min = 46.74 max = 48.82 avg = 48.18
vgg16 min = 286.38 max = 292.93 avg = 289.49
vgg16_int8 min = 313.20 max = 315.49 avg = 314.35
resnet50 min = 105.36 max = 105.80 avg = 105.61
resnet50_int8 min = 101.69 max = 102.31 avg = 101.99
squeezenet_ssd min = 59.38 max = 61.03 avg = 60.00
squeezenet_ssd_int8 min = 56.39 max = 56.64 avg = 56.53
mobilenet_ssd min = 51.75 max = 52.23 avg = 51.94
mobilenet_ssd_int8 min = 45.05 max = 45.67 avg = 45.37
mobilenet_yolo min = 119.79 max = 121.57 avg = 120.77
mobilenetv2_yolov3 min = 80.17 max = 80.41 avg = 80.30
yolov4-tiny min = 110.66 max = 111.11 avg = 110.83
nanodet_m min = 33.79 max = 34.17 avg = 33.93
yolo-fastest-1.1 min = 16.08 max = 16.24 avg = 16.17
yolo-fastestv2 min = 14.55 max = 14.67 avg = 14.59
vision_transformer min = 2020.00 max = 2025.81 avg = 2022.37
FastestDet min = 13.77 max = 14.05 avg = 13.89
