Yolo-Fastest
C LINK⚡ Yolo universal target detection model combined with EfficientNet-lite, the calculation amount is only 230Mflops(0.23Bflops), and the model size is 1.3MB
:zap:Yolo-Fastest:zap:
- 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 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
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=1to build with CUDA to accelerate by using GPU (CUDA should be in/usr/local/cuda)CUDNN=1to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in/usr/local/cudnn)CUDNN_HALF=1to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2xOPENCV=1to 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
Makefileaccording 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.109Thanks
- https://github.com/AlexeyAB/darknet
- https://github.com/Tencent/ncnn