dog-qiuqiu/MobileNet-Yolo：MobileNetV2-YoloV3-Nano：0.5BFl

项目介绍

dog-qiuqiu/MobileNet-Yolo 是一个托管在 GitHub 上的开源项目，主要使用 C 编写，采用 Other 许可证，目前获得 1.7k 个 Star、274 次 Fork。

仓库全名: dog-qiuqiu/MobileNet-Yolo

README 摘要

2021.2.6 此项目不再更新，新项目地址: Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest 此表中NCNN基准未更新最新ARM82数据，最新版本NCNN理论上ARM82会有一倍速度提升，待更新... 添加基于ncnn的106关键点 C sample:https://github.com/dog-qiuqiu/MobileNet-Yolo/tree/master/sample/ncnn Darknet Group convolution is not well supported on some GPUs such as NVIDIA PASCAL…

完整内容见 GitHub 原始 README 或本页。

常见问题

MobileNet-Yolo 用什么语言开发？

主要使用 C 编写，采用 Other 许可证，目前有 1.7k 个 Star。

MobileNet-Yolo 可以免费商用吗？

仓库标注的许可证为 Other，商用前请先阅读仓库 LICENSE 文件确认授权范围。

如何获取 MobileNet-Yolo 的源码？

可以直接克隆仓库：git clone https://github.com/dog-qiuqiu/MobileNet-Yolo.git，默认分支为 master；安装与运行步骤见仓库 README。

MobileNet-Yolo 最近还在维护吗？

仓库最近一次更新为 2021年2月6日，当前有 24 个未关闭 Issue；Star 数为 1.7k，可作为社区活跃度的参考。

关于

GitHub 原始描述（英文）

MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:

cnn computer-vision cv darknet deep-learning face-detection landmark landmark-detection mnn mnn-framework mobilenet-yolo mobilenetv2 ncnn ncnn-model object-detection yolo yolov3

Other
1.7k Stars
274 Forks
33 Watching

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

2324

价值分

1130

活跃状态

活跃

GitHub

语言占比

Batchfile

C++

CMake

Cuda

Makefile

PowerShell

Python

Shell

贡献者

dog-qiuqiu

jyYaYa

2021.2.6 此项目不再更新，新项目地址: Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest

Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest
此表中NCNN基准未更新最新ARM82数据，最新版本NCNN理论上ARM82会有一倍速度提升，待更新…
添加基于ncnn的106关键点 C sample:https://github.com/dog-qiuqiu/MobileNet-Yolo/tree/master/sample/ncnn

***Darknet Group convolution is not well supported on some GPUs such as NVIDIA PASCAL!!!

https://github.com/AlexeyAB/darknet/issues/6091#issuecomment-651667469

针对某些Pascal显卡例如1080ti在darknet上训练失败/训练异常缓慢/推理速度异常的可以采用Pytorch版yolo3框架训练/推理

https://github.com/dog-qiuqiu/yolov3

MobileNetV2-YOLOv3-Lite&Nano Darknet

Mobile inference frameworks benchmark (4*ARM_CPU)

Network	VOC mAP(0.5)	COCO mAP(0.5)	Resolution	Inference time (NCNN/Kirin 990)	Inference time (MNN arm82/Kirin 990)	FLOPS	Weight size
MobileNetV2-YOLOv3-Lite(our)	73.26	37.44	320	28.42 ms	18 ms	1.8BFlops	8.0MB
MobileNetV2-YOLOv3-Nano(our)	65.27	30.13	320	10.16 ms	5 ms	0.5BFlops	3.0MB
MobileNetV2-YOLOv3	70.7	&	352	32.15 ms	& ms	2.44BFlops	14.4MB
MobileNet-SSD	72.7	&	300	26.37 ms	& ms	& BFlops	23.1MB
YOLOv5s	&	56.2	416	150.5 ms	& ms	13.2BFlops	28.1MB
YOLOv3-Tiny-Prn	&	33.1	416	36.6 ms	& ms	3.5BFlops	18.8MB
YOLOv4-Tiny	&	40.2	416	44.6 ms	& ms	6.9BFlops	23.1MB
YOLO-Nano	69.1	&	416	& ms	& ms	4.57BFlops	4.0MB

Support mobile inference frameworks such as NCNN&MNN
The mnn benchmark only includes the forward inference time
The ncnn benchmark is the forward inference time + post-processing time(NMS…) of the convolution feature map.
Darknet Train Configuration: CUDA-version: 10010 (10020), cuDNN: 7.6.4,OpenCV version: 4 GPU:RTX2080ti

MobileNetV2-YOLOv3-Lite-COCO Test results

Application

Ultralight-SimplePose

A ultra-lightweight human body posture key point prediction model designed for mobile devices, which can cooperate with MobileNetV2-YOLOv3-Nano to complete the human body posture estimation task
https://github.com/dog-qiuqiu/Ultralight-SimplePose

YoloFace-500k: 500kb yolo-Face-Detection

Network	Resolution	Inference time (NCNN/Kirin 990)	Inference time (MNN arm82/Kirin 990)	FLOPS	Weight size
UltraFace-version-RFB	320x240	&ms	3.36ms	0.1BFlops	1.3MB
UltraFace-version-Slim	320x240	&ms	3.06ms	0.1BFlops	1.2MB
yoloface-500k	320x256	5.5ms	2.4ms	0.1BFlops	0.52MB
yoloface-500k-v2	352x288	4.7ms	&ms	0.1BFlops	0.42MB

都500k了，要啥mAP:sunglasses:
Inference time (DarkNet/i7-6700):13ms
The mnn benchmark only includes the forward inference time
The ncnn benchmark is the forward inference time + post-processing time(NMS…) of the convolution feature map.

Wider Face Val

Model	Easy Set	Medium Set	Hard Set
libfacedetection v1（caffe）	0.65	0.5	0.233
libfacedetection v2（caffe）	0.714	0.585	0.306
Retinaface-Mobilenet-0.25 (Mxnet)	0.745	0.553	0.232
version-slim-320	0.77	0.671	0.395
version-RFB-320	0.787	0.698	0.438
yoloface-500k-320	0.728	0.682	0.431
yoloface-500k-352-v2	0.768	0.729	0.490

yoloface-500k-v2：The SE&CSP module is added
V2 does not support MNN temporarily
wider_face_val(ap05): yoloface-500k: 53.75 yoloface-500k-v2: 56.69

YoloFace-500k Test results(thresh 0.7)

YoloFace-500k-v2 Test results(thresh 0.7)

YoloFace-50k: Sub-millisecond face detection model

Network	Resolution	Inference time (NCNN/Kirin 990)	Inference time (MNN arm82/Kirin 990)	Inference time (DarkNet/R3-3100)	FLOPS	Weight size
yoloface-50k	56x56	0.27ms	0.31ms	0.5 ms	0.001BFlops	46kb

For the close-range face detection model in a specific scene, the recommended detection distance is 1.5m

YoloFace-50k Test results(thresh 0.7)

YoloFace50k-landmark106(Ultra lightweight 106 point face-landmark model)

Network	Resolution	Inference time (NCNN/Kirin 990)	Inference time (MNN arm82/Kirin 990)	Weight size
landmark106	112x112	0.6ms	0.5ms	1.4MB

Face detection: yoloface-50k Landmark: landmark106

YoloFace50k-landmark106 Test results

Reference&Framework instructions&How to Train

https://github.com/AlexeyAB/darknet
You must use a pre-trained model to train your own data set. You can make a pre-trained model based on the weights of COCO training in this project to initialize the network parameters
交流qq群:1062122604

About model selection

MobileNetV2-YOLOv3-SPP: Nvidia Jeston, Intel Movidius, TensorRT，NPU，OPENVINO…High-performance embedded side
MobileNetV2-YOLOv3-Lite: High Performance ARM-CPU，Qualcomm Adreno GPU， ARM82…High-performance mobile
MobileNetV2-YOLOv3-NANO： ARM-CPU…Computing resources are limited
MobileNetV2-YOLOv3-Fastest： … Can you do personal face detection???It’s better than nothing

NCNN conversion tutorial

Benchmark:https://github.com/Tencent/ncnn/tree/master/benchmark
NCNN supports direct conversion of darknet models
darknet2ncnn: https://github.com/Tencent/ncnn/tree/master/tools/darknet

NCNN C++ Sample

https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/sample/ncnn

NCNN Android Sample

DarkNet2Caffe tutorial

Environmental requirements

Python2.7
python-opencv
Caffe(add upsample layer https://github.com/dog-qiuqiu/caffe)

You have to compile cpu version of caffe！！！

  cd darknet2caffe/
  python darknet2caffe.py MobileNetV2-YOLOv3-Nano-voc.cfg MobileNetV2-YOLOv3-Nano-voc.weights MobileNetV2-YOLOv3-Nano-voc.prototxt MobileNetV2-YOLOv3-Nano-voc.caffemodel
  cp MobileNetV2-YOLOv3-Nano-voc.prototxt sample
  cp MobileNetV2-YOLOv3-Nano-voc.caffemodel sample
  cd sample
  python detector.py

MNN conversion tutorial

Benchmark:https://www.yuque.com/mnn/cn/tool_benchmark
Convert darknet model to caffemodel through darknet2caffe
Manually replace the upsample layer in prototxt with the interp layer
Take the modification of MobileNetV2-YOLOv3-Nano-voc.prototxt as an example

	#layer {
	#    bottom: "layer71-route"
	#    top: "layer72-upsample"
	#    name: "layer72-upsample"
	#    type: "Upsample"
	#    upsample_param {
	#        scale: 2
	#    }
	#}
	layer {
	    bottom: "layer71-route"
	    top: "layer72-upsample"
	    name: "layer72-upsample"
	    type: "Interp"
	    interp_param {
		height:20  #upsample h size
		width:20   #upsample w size
	    }
	}

MNN conversion: https://www.yuque.com/mnn/cn/model_convert

Thanks

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MobileNet-Yolo