2 YOLOv6 量化感知蒸馏框架 针 对 YOLOv6s， 我 们 选 择 对 Neck（Rep-PAN）输 出 的 特 征 图 进 行 通 道 蒸 馏 （channel-wise distillation, CW)。另外，我们采用“自蒸馏”的方法，教师模型是 FP32 精度的 YOLOv6s，学生模型是 INT8 精度的 YOLOv6s。下图 7 是一个简化 示意图，只画出了 Neck Nsight-systems: https://docs.nvidia.com/nsight-systems/UserGuide/index.html [6] Channel-wise Knowledge Distillation for Dense Prediction, https://arxiv.org/ abs/2011.13256 [7] YOLOv6: A Single-Stage Object Detection https://tech.meituan.com/2021/07/08/multi-business-modeling.html. [7] Tang, Jiaxi, and Ke Wang. “Ranking distillation: Learning compact ranking models with high performance for recommender system.” Proceedings

application-independent set of baked geometric results. This ‘distillation’ of scenes into baked geometry is exactly analogous to the distillation of lighting and rendering scenes into rendered image data

Socher, R. (2018). A closer look at deep learn‐ ing heuristics: learning rate restarts, warmup and distillation. arXiv preprint arXiv:1810.13243. [Graves, 2013] Graves, A. (2013). Generating sequences with