# shufflev2-yolov5
**Repository Path**: ComputerVision3S/shufflev2-yolov5
## Basic Information
- **Project Name**: shufflev2-yolov5
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-08-27
- **Last Updated**: 2021-12-28
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# shufflev2-yolov5：lighter, faster and easier to deploy 
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
ID|Model | Input_size|Flops| Params | Size（M） |Map@0.5|Map@.5:0.95
:-----:|:-----:|:-----:|:----------:|:----:|:----:|:----:|:----:|
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| YOLOv5ss(ours)| 320×320|1.43G |1.62M|3.3| 36.2|20.5|
004| nanodet-m| 416×416| 1.2G |0.95M|1.8|- |23.5
005| YOLOv5ss(ours)| 416×416|2.42G |1.62M|3.3| 41.2|24.0|
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
### Table with coco api
```
# evaluate in 640×640：
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.268
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.451
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.273
# evaluate in 416×416：
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.240
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.412
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.239
# evaluate in 320×320：
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.205
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.362
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.202
```
### Comparison on different platforms
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
### Detection effect
Pytorch{640×640}：
NCNN{FP16}@{640×640}:
NCNN{Int8}@{640×640}:
### Base on YOLOv5
### 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
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
```
### NCNN_Android_demo
This is a Redmi phone, the processor is Snapdragon 730G, and shufflev2-yolov5 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
NCNN deployment and int8 quantization：https://zhuanlan.zhihu.com/p/400975662
## Reference
https://github.com/Tencent/ncnn
https://github.com/ultralytics/yolov5
https://github.com/megvii-model/ShuffleNet-Series