⚡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 | 60.8 | 320 | 6.74ms | 4.42ms | 0.23BFlops | 1.3MB |
Yolo-Fastest-XL | 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