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YOLOv6 2.1
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@Chilicyy Chilicyy released this 04 Nov 14:03
· 183 commits to main since this release
0.2.1
9ac9ad4

v2.1 release

Features

Release base models

  • 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, 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.

Performance

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
Assets 11