YOLOv6 2.0
v2.0 release
YOLOv6 has a series of models for various industrial scenarios, including nano/tiny/s/m/l, which the architectures vary considering the model size for better accuracy-speed trade-off. And some Bag-of-freebies methods are introduced to further improve the performance, such as self-distillation and more training epochs. For industrial deployment, we adopt QAT with channel-wise distillation and graph optimization to pursue extreme performance.
New Features
- Release M/L models and update N/T/S models with enhanced performance.⭐️ Benchmark
- 2x faster training time.
- Fix the degration of performance when evaluating on 640x640 inputs.
- Customized quantization methods. 🚀 Quantization Tutorial