在yolov5上进行一系列的消融实验，使其更轻（Flops更小，内存更低，参数更少）和更快（添加shuffle channel，yolov5 head for channel reduce。在input_size为320×320的树莓派4B上可以推断至少每秒 10 帧）并且更容易部署（去除 Focus 层和四次切片操作，将模型量化精度降低到可接受的范围内）。
| ID | Model | Input_size | Flops | Params | Size（M） | [email protected] | [email protected]:0.95 |
|---|---|---|---|---|---|---|---|
| 001 | yolo-faster | 320×320 | 0.25G | 0.35M | 1.4 | 24.4 | - |
| 002 | nanodet-m | 320×320 | 0.72G | 0.95M | 1.8 | - | 20.6 |
| 003 | shufflev2-yolov5 | 320×320 | 1.43G | 1.62M | 3.3 | 35.5 | - |
| 004 | nanodet-m | 416×416 | 1.2G | 0.95M | 1.8 | - | 23.5 |
| 005 | shufflev2-yolov5 | 416×416 | 2.42G | 1.62M | 3.3 | 40.5 | 23.5 |
| 006 | yolov4-tiny | 416×416 | 5.62G | 8.86M | 33.7 | 40.2 | 21.7 |
| 007 | yolov3-tiny | 416×416 | 6.96G | 6.06M | 23.0 | 33.1 | 16.6 |
| Equipment | Computing backend | System | Framework | Input | Speed{our} | Speed{yolov5s} |
|---|---|---|---|---|---|---|
| Inter | @i5-10210U | window(x86) | 640×640 | torch-cpu | 112ms | 179ms |
| Nvidia | @RTX 2080Ti | Linux(x86) | 640×640 | torch-gpu | 11ms | 13ms |
| Raspberrypi 4B | @ARM Cortex-A72 | Linux(arm64) | 320×320 | ncnn | 97ms | 371ms |
Pytorch{640×640}：
NCNN{FP16}@{640×640}:
NCNN{Int8}@{640×640}:
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
shufflev2-yolov5 min = 90.86 max = 93.53 avg = 91.56
shufflev2-yolov5-int8 min = 83.15 max = 84.17 avg = 83.65
shufflev2-yolov5-416 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 = 84.03 max = 87.68 avg = 86.32
nanodet_m-416 min = 143.89 max = 145.06 avg = 144.67
shufflev2-yolov5 min = 84.30 max = 86.34 avg = 85.79
shufflev2-yolov5-int8 min = 80.98 max = 82.80 avg = 81.25
shufflev2-yolov5-416 min = 142.75 max = 146.10 avg = 144.34
yolov4-tiny min = 276.09 max = 289.83 avg = 285.99
nanodet_m min = 81.15 max = 81.71 avg = 81.33
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
Detailed model link: https://zhuanlan.zhihu.com/p/400545131
NCNN deployment and int8 quantization：https://zhuanlan.zhihu.com/p/400975662
https://github.com/Tencent/ncnn