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https://github.com/dog-qiuqiu/MobileNet-Yolo

MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:
https://github.com/dog-qiuqiu/MobileNet-Yolo

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

Last synced: 12 days ago
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MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:

Host: GitHub
URL: https://github.com/dog-qiuqiu/MobileNet-Yolo
Owner: dog-qiuqiu
License: other
Created: 2020-06-17T05:13:42.000Z (about 4 years ago)
Default Branch: master
Last Pushed: 2021-02-06T02:16:46.000Z (over 3 years ago)
Last Synced: 2024-02-29T08:34:17.962Z (4 months ago)
Topics: 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
Language: C
Homepage:
Size: 138 MB
Stars: 1,674
Watchers: 35
Forks: 274
Open Issues: 26
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-stars - dog-qiuqiu/MobileNet-Yolo - MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire: (C)
awesome-yolo-object-detection - dog-qiuqiu/MobileNet-Yolo - qiuqiu/MobileNet-Yolo?style=social"/> : MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB🔥🔥🔥. (Lighter and Deployment Frameworks)

README

        ## 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

![image](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/master/data/7a96026f319ad31e28bc55458ee97e97.gif)

* ***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](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/MobileNetV2-YOLOv3-Lite)(our)|73.26|37.44|320|28.42 ms|18 ms|1.8BFlops|8.0MB

[MobileNetV2-YOLOv3-Nano](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/MobileNetV2-YOLOv3-Nano)(our)|65.27|30.13|320|10.16 ms|5 ms|0.5BFlops|3.0MB

[MobileNetV2-YOLOv3](https://github.com/eric612/MobileNet-YOLO)|70.7|&|352|32.15 ms|& ms|2.44BFlops|14.4MB

[MobileNet-SSD](https://github.com/chuanqi305/MobileNet-SSD)|72.7|&|300|26.37 ms|& ms|& BFlops|23.1MB

[YOLOv5s](https://github.com/ultralytics/yolov5)|&|56.2|416|150.5 ms|& ms|13.2BFlops|28.1MB

[YOLOv3-Tiny-Prn](https://github.com/AlexeyAB/darknet#pre-trained-models)|&|33.1|416|36.6 ms|& ms|3.5BFlops|18.8MB

[YOLOv4-Tiny](https://github.com/AlexeyAB/darknet#pre-trained-models)|&|40.2|416|44.6 ms|& ms|6.9BFlops|23.1MB

[YOLO-Nano](https://github.com/liux0614/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

![image](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/master/data/predictions.jpg)

# 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

![image](https://github.com/dog-qiuqiu/Ultralight-SimplePose/blob/master/data/Figure_1-1.jpg)

## 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](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/yoloface-500k/v1)|320x256|5.5ms|2.4ms|0.1BFlops|0.52MB

[yoloface-500k-v2](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/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](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/yoloface-500k/v1)|**0.728**|**0.682**|**0.431**|

[yoloface-500k-352-v2](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/yoloface-500k/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)

![image](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/master/data/p1.jpg)

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

![image](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/master/data/f2.jpg)

## 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](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/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)

![image](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/master/data/yoloface-50k-1.jpg)

## 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](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/yoloface50k-landmark106)|112x112|0.6ms|0.5ms|1.4MB

* Face detection: yoloface-50k Landmark: landmark106

## YoloFace50k-landmark106 Test results

![image](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/master/yoloface50k-landmark106/yoloface-50k-landmark106.jpg)

## 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

![image](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/blob/master/data/MobileNetV2-YOLOV3-Nano.gif)

* https://github.com/dog-qiuqiu/Android_MobileNetV2-YOLOV3-Nano-NCNN

* APK:https://github.com/dog-qiuqiu/Android_MobileNetV2-YOLOV3-Nano-NCNN/blob/master/app/release/MobileNetv2-yolov3-nano.apk

## 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

* https://github.com/shicai/MobileNet-Caffe

* https://github.com/WZTENG/YOLOv5_NCNN 

* https://github.com/AlexeyAB/darknet

* https://github.com/Tencent/ncnn

* https://gluon-cv.mxnet.io/