YOLOv6 base model

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Features

• This is a RepOpt-version implementation of YOLOv6 according to QARepVGG.

• The QARep version models possess slightly lower float accuracy on COCO than the RepVGG version models, but achieve highly improved quantized accuracy.

• The INT8 accuracies listed were obtained using a simple PTQ process, as implemented in the onnx_to_trt.py script. However, higher accuracies could be achieved using Quantization-Aware Training (QAT) due to the specific architecture design of the QARepVGG model.

Performance

Model	Size	Float mAPval 0.5:0.95	INT8 mAPval 0.5:0.95	SpeedT4 trt fp16 b32 (fps)	SpeedT4 trt int8 b32 (fps)	Params (M)	FLOPs (G)
YOLOv6-N	640	37.5	34.3	1286	1773	4.7	11.4
YOLOv6-N-qa	640	37.1	36.4	1286	1773	4.7	11.4
YOLOv6-S	640	45.0	41.3	513	1117	18.5	45.3
YOLOv6-S-qa	640	44.7	44.0	513	1117	18.5	45.3
YOLOv6-M	640	50.0	48.1	250	439	34.9	85.8
YOLOv6-M-qa	640	49.7	49.4	250	439	34.9	85.8

• Speed is tested with TensorRT 8.4 on T4.
• We have not conducted experiments on the YOLOv6-L model since it does not use the RepVGG architecture.
• The processes of model training, evaluation, and inference are the same as the original ones. For details, please refer to this README.