Yoloface-500k:ultra-light real-time face detection model, 500kb

待办

• 开放andooid示例项目(求大佬)

MobileNetv2-YOLOv3-SPP Darknet

A darknet implementation of MobileNetv2-YOLOv3-SPP detection network

*emmmm...这个懒得训练，mAP就凑合这样吧

Darknet Group convolution is not well supported on some GPUs such as NVIDIA PASCAL!!! The MobileNetV2-YOLOv3-SPP inference time is 100ms at GTX1080ti, but RTX2080 inference time is 5ms!!!

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

MobileNetV2-YOLOv3-Lite&Nano Darknet

Mobile inference frameworks benchmark (4*ARM_CPU)

• 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

MobileNetV2-YOLO-Fastest

• 都2.4ms了，要啥mAP :sunglasses:
• Suitable for hardware with extremely tight computing resources
• 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.
• This model is recommended to do some simple single object detection suitable for simple application scenarios

MobileNetV2-YOLO-Fastest Test results

500kb的yolo-Face-Detection

• 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

YoloFace-500k 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

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

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 Android Sample

• 白嫖中....

Thanks

• https://github.com/shicai/MobileNet-Caffe
• https://github.com/AlexeyAB/darknet
• https://github.com/Tencent/ncnn
• https://gluon-cv.mxnet.io/