YOLOv6 base model

Use only regular convolution and Relu activation functions.
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.
Suitable for users who are just getting started or who need to apply, optimize and deploy an 8-bit quantization model quickly and frequently.

Model	Size	mAP^val 0.5:0.95	Speed^{T4 TRT FP16 b1 (FPS)}	Speed^{T4 TRT FP16 b32 (FPS)}	Speed^{T4 TRT INT8 b1 (FPS)}	Speed^{T4 TRT INT8 b32 (FPS)}	Params ^(M)	FLOPs ^(G)
YOLOv6-N-base	640	36.6^distill	727	1302	814	1805	4.65	11.46
YOLOv6-S-base	640	45.3^distill	346	525	487	908	13.14	30.6
YOLOv6-M-base	640	49.4^distill	179	245	284	439	28.33	72.30
YOLOv6-L-base	640	51.1^distill	116	157	196	288	59.61	150.89

Speed is tested with TensorRT 8.2.4.2 on T4.
The processes of model training, evaluation, and inference are the same as the original ones. For details, please refer to this README.

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