YOLOv6 4.0
v4.0 release
Features
- Release YOLOv6Lite models on mobile or CPU.
- Update MBLABlock in the network structure.
- Update YOLOv6Lite-face models on mobile or CPU.
- Code reconstruction and normalization of convolution operators.
Performance of YOLOv6Lite models
| Model | Size | mAPval 0.5:0.95 |
sm8350 (ms) |
mt6853 (ms) |
sdm660 (ms) |
Params (M) |
FLOPs (G) |
|---|---|---|---|---|---|---|---|
| YOLOv6Lite-S | 320*320 | 22.4 | 7.99 | 11.99 | 41.86 | 0.55 | 0.56 |
| YOLOv6Lite-M | 320*320 | 25.1 | 9.08 | 13.27 | 47.95 | 0.79 | 0.67 |
| YOLOv6Lite-L | 320*320 | 28.0 | 11.37 | 16.20 | 61.40 | 1.09 | 0.87 |
| YOLOv6Lite-L | 320*192 | 25.0 | 7.02 | 9.66 | 36.13 | 1.09 | 0.52 |
| YOLOv6Lite-L | 224*128 | 18.9 | 3.63 | 4.99 | 17.76 | 1.09 | 0.24 |
Table Notes
- From the perspective of model size and input image ratio, we have built a series of models on the mobile terminal to facilitate flexible applications in different scenarios.
- All checkpoints are trained with 400 epochs without distillation.
- Results of the mAP and speed are evaluated on COCO val2017 dataset, and the input resolution is the Size in the table.
- Speed is tested on MNN 2.3.0 AArch64 with 2 threads by arm82 acceleration. The inference warm-up is performed 10 times, and the cycle is performed 100 times.
- Qualcomm 888(sm8350), Dimensity 720(mt6853) and Qualcomm 660(sdm660) correspond to chips with different performances at the high, middle and low end respectively, which can be used as a reference for model capabilities under different chips.
- Refer to Test NCNN Speed tutorial to reproduce the NCNN speed results of YOLOv6Lite.
Performance of YOLOv6_MBLA models
| Model | Size | mAPval 0.5:0.95 |
SpeedT4 trt fp16 b1 (fps) |
SpeedT4 trt fp16 b32 (fps) |
Params (M) |
FLOPs (G) |
|---|---|---|---|---|---|---|
| YOLOv6-S-mbla | 640 | 47.0distill | 300 | 424 | 11.6 | 29.8 |
| YOLOv6-M-mbla | 640 | 50.3distill | 168 | 216 | 26.1 | 66.7 |
| YOLOv6-L-mbla | 640 | 52.0distill | 129 | 154 | 46.3 | 118.2 |
| YOLOv6-X-mbla | 640 | 53.5distill | 78 | 94 | 78.8 | 199.0 |
Table Notes
- Speed is tested with TensorRT 8.4.2.4 on T4.
- The processes of model training, evaluation, and inference are the same as the original ones. For details, please refer to this README.