TimVerion / Yolo-Fastest

:zap: Yolo universal target detection model combined with EfficientNet-lite, the calculation amount is only 230Mflops(0.23Bflops), and the model size is 1.3MB

⚡Yolo-Fastest⚡

• Simple, fast, compact, easy to transplant
• A real-time target detection algorithm for all platforms
• The fastest and smallest known universal target detection algorithm based on yolo
• Optimized design for ARM mobile terminal, optimized to support NCNN reasoning framework
• The speed is 45% faster than mobilenetv2-yolov3-nano, and the parameter amount is reduced by 56%

Evaluating indicator/Benchmark

Network	VOC mAP(0.5)	COCO mAP(0.5)	Resolution	Run Time(Ncnn 1xCore)	Run Time(Ncnn 4xCore)	FLOPS	Weight size
MobileNetV2-YOLOv3-Nano	65.27	30.13	320	11.36ms	5.48ms	0.55BFlops	3.0MB
Yolo-Fastest(our)	61.02	&	320	6.74ms	4.42ms	0.23BFlops	1.3MB
Yolo-Fastest-XL(our)	69.43	32.45	320	15.15ms	7.09ms	0.70BFlops	3.5MB

• Test platform Kirin 990 CPU，Based on NCNN
• Suitable for hardware with extremely tight computing resources
• This model is recommended to do some simple single object detection suitable for simple application scenarios

Pascal VOC performance index comparison

Network	Model Size	mAP(VOC 2017)	FLOPS
Tiny YOLOv2	60.5MB	57.1%	6.97BFlops
Tiny YOLOv3	33.4MB	58.4%	5.52BFlops
YOLO Nano	4.0MB	69.1%	4.51Bflops
MobileNetv2-SSD-Lite	13.8MB	68.6%	&Bflops
Yolo Fastest	1.3MB	61.02%	0.23Bflops
Yolo Fastest-XL	3.5MB	69.43%	0.70Bflops

• Performance indicators reference from the papers and public indicators in the github project

Compile

How to compile on Linux

• This repo is based on Darknet project so the instructions for compiling the project are same (https://github.com/MuhammadAsadJaved/darknet#how-to-compile-on-windows-legacy-way)

Just do make in the Yolo-Fastest-master directory. Before make, you can set such options in the Makefile: link

• GPU=1 to build with CUDA to accelerate by using GPU (CUDA should be in /usr/local/cuda)
• CUDNN=1 to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in /usr/local/cudnn)
• CUDNN_HALF=1 to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x
• OPENCV=1 to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams
• Set the other options in the Makefile according to your need.

Test/Demo

*Run Yolo-Fastest , Yolo-Fastest-x1 , Yolov3 or Yolov4 on image or video inputs

Demo on image input

*Note: change .data , .cfg , .weights and input image file in image_yolov3.sh for Yolo-Fastest-x1, Yolov3 and Yolov4

  sh image_yolov3.sh

Demo on video input

*Note: Use any input video and place in the data folder or use 0 in the video_yolov3.sh for webcam

*Note: change .data , .cfg , .weights and input video file in video_yolov3.sh for Yolo-Fastest-x1, Yolov3 and Yolov4

  sh video_yolov3.sh

How to Train

Generate a pre-trained model for the initialization of the model backbone

  ./darknet partial yolo-fastest.cfg yolo-fastest.weights yolo-fastest.conv.109 109

Train

• 交流qq群:1062122604
• https://github.com/AlexeyAB/darknet

  ./darknet detector train voc.data yolo-fastest.cfg yolo-fastest.conv.109 

Thanks

• https://github.com/AlexeyAB/darknet
• https://github.com/Tencent/ncnn