# shufflev2-yolov5 **Repository Path**: chaucerg/shufflev2-yolov5 ## Basic Information - **Project Name**: shufflev2-yolov5 - **Description**: shufflev2-yolov5：lighter, faster and easier to deploy - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-11-13 - **Last Updated**: 2021-11-13 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # shufflev2-yolov5：lighter, faster and easier to deploy ![0111](https://user-images.githubusercontent.com/82716366/129756605-a0cba66c-b296-43f1-b83e-39f5f10cd1c2.jpg) 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）。 ### 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| 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 ### 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}： ![person](https://user-images.githubusercontent.com/82716366/129822414-56ad3bf3-1005-47aa-9bd9-c4117227a981.jpg)
NCNN{FP16}@{640×640}: ![image](https://user-images.githubusercontent.com/82716366/129720264-ca6403c4-188b-4dd1-8372-c9a18c915fd8.png)
NCNN{Int8}@{640×640}: ![image](https://user-images.githubusercontent.com/82716366/129720338-5ce1ee2e-46ab-4e82-a2d1-2d8faf6c5661.png)
### Base on YOLOv5 ![image](https://user-images.githubusercontent.com/82716366/129720466-728043a3-5af7-40be-9cb4-f96d42d5bdaf.png) ### 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 ``` ### More detailed explanation Detailed model link: https://zhuanlan.zhihu.com/p/400545131 ![image](https://user-images.githubusercontent.com/82716366/129891972-31f230e3-6e30-4392-820e-6aef08a51ab1.png) NCNN deployment and int8 quantization：https://zhuanlan.zhihu.com/p/400975662 ![image](https://user-images.githubusercontent.com/82716366/129892035-4a814109-1daa-4d74-bf3c-d1e74463240e.png) ## Reference https://github.com/Tencent/ncnn https://github.com/ultralytics/yolov5 https://github.com/megvii-model/ShuffleNet-Series