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Use only regular convolution and Relu activation functions.
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Apply CSP (1/2 channel dim) blocks in the network structure, except for Nano base model.
Advantage:
- Adopt a unified network structure and configuration, and the accuracy loss of the PTQ 8-bit quantization model is negligible, about 0.4%.
- Suitable for users who are just getting started or who need to apply, optimize and deploy an 8-bit quantization model quickly and frequently.
Shortcoming:
- The accuracy on COCO is slightly lower than the v2.0 released models.
| Model | Size | mAPval 0.5:0.95 |
SpeedT4 trt fp16 b1 (fps) |
SpeedT4 trt fp16 b32 (fps) |
Params (M) |
FLOPs (G) |
|---|---|---|---|---|---|---|
| YOLOv6-N-base | 640 | 35.6400e | 832 | 1249 | 4.3 | 11.1 |
| YOLOv6-S-base | 640 | 43.8400e | 373 | 531 | 11.5 | 27.6 |
| YOLOv6-M-base | 640 | 48.8distill | 179 | 246 | 27.7 | 68.4 |
| YOLOv6-L-base | 640 | 51.0distill | 115 | 153 | 58.5 | 144.0 |
- Speed is tested with TensorRT 7.2 on T4.
- The processes of model training, evaluation, and inference are the same as the original ones. For details, please refer to this README.