maoweinuaa / 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

Network	VOC mAP(0.5)	Resolution	Run Time(Ncnn 1xCore)	Run Time(Ncnn 4xCore)	FLOPS	Weight size
MobileNetV2-YOLOv3-Nano	65.27	320	11.36ms	5.48ms	0.55BFlops	3.0MB
Yolo-Fastest(our)	61.02	320	6.74ms	4.42ms	0.23BFlops	1.3MB
MobileNetV2 SSD-Lite	68.6	300	&ms	&ms	&BFlops	13.8MB
Yolo-Fastest-XL(our)	68.8	320	15.15ms	7.09ms	0.70BFlops	3.5MB

• Test platform Kirin 990，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

Test

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