YOLOv5-Lite:lighter, faster and easier to deploy

YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, and fewer parameters) and faster (add shuffle channel, yolov5 head for channel reduce. It can infer at least 10+ FPS On the Raspberry Pi 4B when input the frame with 320×320) and is easier to deploy (removing the Focus layer and four slice operations, reducing the model quantization accuracy to an acceptable range).

Comparison of ablation experiment results

IDModelInput_sizeFlopsParamsSize(M)[email protected][email protected]:0.95
001yolo-fastest320×3200.25G0.35M1.424.4-
002nanodet-m320×3200.72G0.95M1.8-20.6
003yolo-fastest-xl320×3200.72G0.92M3.534.3-
004yolov5-lite320×3201.43G1.62M3.336.220.8
005yolov3-tiny416×4166.96G6.06M23.033.116.6
006yolov4-tiny416×4165.62G8.86M33.740.221.7
007nanodet-m416×4161.2G0.95M1.8-23.5
008yolov5-lite416×4162.42G1.62M3.341.324.4
009yolov5-lite640×6402.42G1.62M3.345.727.1
010yolov5s640×64017.0G7.3M14.255.436.7

Comparison on different platforms

EquipmentComputing backendSystemFrameworkInputSpeed{our}Speed{yolov5s}
Inter@i5-10210Uwindow(x86)640×640torch-cpu112ms179ms
Nvidia@RTX 2080TiLinux(x86)640×640torch-gpu11ms13ms
Raspberrypi 4B@ARM Cortex-A72Linux(arm64)320×320ncnn97ms371ms

Detection effect

Pytorch{640×640}:

person


NCNN{FP16}@{640×640}:

image


NCNN{Int8}@{640×640}:

image


Base on YOLOv5

image

10FPS can be used with yolov5 on the Raspberry Pi with only 0.1T computing power

Excluding the first three warm-ups, the device temperature is stable above 45°, the forward reasoning framework is ncnn, and the two benchmark comparisons are recorded

# 第四次
pi@raspberrypi:~/Downloads/ncnn/build/benchmark $ ./benchncnn 8 4 0
loop_count = 8
num_threads = 4
powersave = 0
gpu_device = -1
cooling_down = 1
         yolov5-lite  min =   90.86  max =   93.53  avg =   91.56
    yolov5-lite-int8  min =   83.15  max =   84.17  avg =   83.65
     yolov5-lite-416  min =  154.51  max =  155.59  avg =  155.09
         yolov4-tiny  min =  298.94  max =  302.47  avg =  300.69
           nanodet_m  min =   86.19  max =  142.79  avg =   99.61
          squeezenet  min =   59.89  max =   60.75  avg =   60.41
     squeezenet_int8  min =   50.26  max =   51.31  avg =   50.75
           mobilenet  min =   73.52  max =   74.75  avg =   74.05
      mobilenet_int8  min =   40.48  max =   40.73  avg =   40.63
        mobilenet_v2  min =   72.87  max =   73.95  avg =   73.31
        mobilenet_v3  min =   57.90  max =   58.74  avg =   58.34
          shufflenet  min =   40.67  max =   41.53  avg =   41.15
       shufflenet_v2  min =   30.52  max =   31.29  avg =   30.88
             mnasnet  min =   62.37  max =   62.76  avg =   62.56
     proxylessnasnet  min =   62.83  max =   64.70  avg =   63.90
     efficientnet_b0  min =   94.83  max =   95.86  avg =   95.35
   efficientnetv2_b0  min =  103.83  max =  105.30  avg =  104.74
        regnety_400m  min =   76.88  max =   78.28  avg =   77.46
           blazeface  min =   13.99  max =   21.03  avg =   15.37
           googlenet  min =  144.73  max =  145.86  avg =  145.19
      googlenet_int8  min =  123.08  max =  124.83  avg =  123.96
            resnet18  min =  181.74  max =  183.07  avg =  182.37
       resnet18_int8  min =  103.28  max =  105.02  avg =  104.17
             alexnet  min =  162.79  max =  164.04  avg =  163.29
               vgg16  min =  867.76  max =  911.79  avg =  889.88
          vgg16_int8  min =  466.74  max =  469.51  avg =  468.15
            resnet50  min =  333.28  max =  338.97  avg =  335.71
       resnet50_int8  min =  239.71  max =  243.73  avg =  242.54
      squeezenet_ssd  min =  179.55  max =  181.33  avg =  180.74
 squeezenet_ssd_int8  min =  131.71  max =  133.34  avg =  132.54
       mobilenet_ssd  min =  151.74  max =  152.67  avg =  152.32
  mobilenet_ssd_int8  min =   85.51  max =   86.19  avg =   85.77
      mobilenet_yolo  min =  327.67  max =  332.85  avg =  330.36
  mobilenetv2_yolov3  min =  221.17  max =  224.84  avg =  222.60

# 第八次
pi@raspberrypi:~/Downloads/ncnn/build/benchmark $ ./benchncnn 8 4 0
loop_count = 8
num_threads = 4
powersave = 0
gpu_device = -1
cooling_down = 1
           nanodet_m  min =   81.15  max =   81.71  avg =   81.33
       nanodet_m-416  min =  143.89  max =  145.06  avg =  144.67
         yolov5-lite  min =   84.30  max =   86.34  avg =   85.79
    yolov5-lite-int8  min =   80.98  max =   82.80  avg =   81.25
     yolov5-lite-416  min =  142.75  max =  146.10  avg =  144.34
         yolov4-tiny  min =  276.09  max =  289.83  avg =  285.99
          squeezenet  min =   59.37  max =   61.19  avg =   60.35
     squeezenet_int8  min =   49.30  max =   49.66  avg =   49.43
           mobilenet  min =   72.40  max =   74.13  avg =   73.37
      mobilenet_int8  min =   39.92  max =   40.23  avg =   40.07
        mobilenet_v2  min =   71.57  max =   73.07  avg =   72.29
        mobilenet_v3  min =   54.75  max =   56.00  avg =   55.40
          shufflenet  min =   40.07  max =   41.13  avg =   40.58
       shufflenet_v2  min =   29.39  max =   30.25  avg =   29.86
             mnasnet  min =   59.54  max =   60.18  avg =   59.96
     proxylessnasnet  min =   61.06  max =   62.63  avg =   61.75
     efficientnet_b0  min =   91.86  max =   95.01  avg =   92.84
   efficientnetv2_b0  min =  101.03  max =  102.61  avg =  101.71
        regnety_400m  min =   76.75  max =   78.58  avg =   77.60
           blazeface  min =   13.18  max =   14.67  avg =   13.79
           googlenet  min =  136.56  max =  138.05  avg =  137.14
      googlenet_int8  min =  118.30  max =  120.17  avg =  119.23
            resnet18  min =  164.78  max =  166.80  avg =  165.70
       resnet18_int8  min =   98.58  max =   99.23  avg =   98.96
             alexnet  min =  155.06  max =  156.28  avg =  155.56
               vgg16  min =  817.64  max =  832.21  avg =  827.37
          vgg16_int8  min =  457.04  max =  465.19  avg =  460.64
            resnet50  min =  318.57  max =  323.19  avg =  320.06
       resnet50_int8  min =  237.46  max =  238.73  avg =  238.06
      squeezenet_ssd  min =  171.61  max =  173.21  avg =  172.10
 squeezenet_ssd_int8  min =  128.01  max =  129.58  avg =  128.84
       mobilenet_ssd  min =  145.60  max =  149.44  avg =  147.39
  mobilenet_ssd_int8  min =   82.86  max =   83.59  avg =   83.22
      mobilenet_yolo  min =  311.95  max =  374.33  avg =  330.15
  mobilenetv2_yolov3  min =  211.89  max =  286.28  avg =  228.01

NCNN_Android_demo

This is a Redmi phone, the processor is Snapdragon 730G, and yolov5-lite is used for detection. The performance is as follows:


This is the quantized int8 model:


Outdoor scene example:


More detailed explanation

Detailed model link: https://zhuanlan.zhihu.com/p/400545131

image

NCNN deployment and int8 quantization:https://zhuanlan.zhihu.com/p/400975662

int8

GitHub

GitHub - ppogg/YOLOv5-Lite: ???YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
???YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size ...