ncnn-benchmark

The benchmark of ncnn that is a high-performance neural network inference framework optimized for the mobile platform https://github.com/Tencent/ncnn

Hardware Platform

Device	System	CPU-Family	CPU-Num	Freq
RK3288	Android 5.1	Cortex-A17	4	1.8GHz
Qualcomm820	Android 6.0	Kryo	2+2	2.15GHz/1.6GHz
Mi5	Android 7.1.2	Kryo	2+2	1.8GHz/1.3GHz
Hi3519	Linux 3.18.20	Cortex-A17	1	1.2GHz

Runtime Environment

1.Using the cpu working in performance model.  

echo performance > /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor

2.Loop a hundred times to take the minimum value of Inference time-consuming.

Result

Devices	Models	Input Size	Single-Thread(ms)	Multi-Threads(ms)
RK3288	SqueezeNet v1.1	227x227x3	194	73
	MobileNet v1.0	224x224x3	324	115
	ResNet18	224x224x3	759	250
	ResNet50	224x224x3	1855	810
	GoogleNet v1.0	224x224x3	701	231
	VGG16	224x224x3	3449	1506
	MobileNet-SSD	300x300x3	652	245
Qualcomm820	SqueezeNet v1.1	227x227x3	91	47
	MobileNet v1.0	224x224x3	150	70
	ResNet18	224x224x3	355	152
	ResNet50	224x224x3	735	309
	GoogleNet v1.0	224x224x3	371	161
	VGG16	224x224x3	1928	769
	MobileNet-SSD	300x300x3	330	160
Mi5	SqueezeNet v1.1	227x227x3	98	51
	MobileNet v1.0	224x224x3	189	79
Hi3519	SqueezeNet v1.1	227x227x3	397	null
	MobileNet v1.0	224x224x3	666	null
	ResNet18	224x224x3	1608	null
	GoogleNet v1.0	224x224x3	1410	null

User Guide

1. Build the benchmark demo

build demo for Linux-x86

./build.sh linux

build demo for Android

./build.sh android

2. How to run the executable files.

If you build demo for linux success,and want to run the benchmark demo.

$ cp ./models/classification/squeezenet.param  ./build-linux/install/bin/
$ cp ./models/classification/squeezenet.bin  ./build-linux/install/bin/
$ cd ./build-linux/install/bin/
$ ./ncnn_classify squeezenet.param squeezenet.bin 227 227 1 1

Demo running params:

./ncnn_classify <ncnn-param-file> <ncnn-model-bin-file> <input-width> <input-height> <loops-num> <threads-num>

Example:

bug1989@ubuntu:~/ncnn-benchmark/build-linux/install/bin$ ./ncnn_classify squeezenet.param squeezenet.bin 227 227 10 2
--- NCNN Classification Benchmark Demo --- 22:41:09 Dec 26 2017
Loops   : 10
Threads : 2
Time cost: Max 263.338 ms, Min 247.209 ms, Avg 252.652 ms.

The End

Thanks to ncnn's author nihui and all the contributors for sharing this framework.