We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation. FMAS subsamples the structure and pre-trained parameters of DeepLabV3+, without fine-tuning, dramatically reducing training time during search. To further reduce candidate evaluation time, we use a subset of the validation dataset during the search. Only the final, Pareto non-dominated, candidates are ultimately fine-tuned using the complete training set. We evaluate FMAS by searching for models that effectively trade accuracy and computational cost on the PASCAL VOC 2012 dataset. FMAS finds competitive designs quickly, e.g., taking just 0.5 GPU days to discover a DeepLabV3+ variant that reduces FLOPs and parameters by 10$\%$ and 20$\%$ respectively, for less than 3$\%$ increased error. We also search on an edge device called GAP8 and use its latency as the metric. FMAS is capable of finding 2.2$\times$ faster network with 7.61$\%$ MIoU loss.

翻译：我们提出FMAS——一个面向语义分割的快速多目标神经网络架构搜索框架。FMAS对DeepLabV3+的结构和预训练参数进行子采样，无需微调即可大幅缩短搜索过程中的训练时间。为进一步减少候选模型评估时间，搜索阶段仅使用验证集的子集，最终仅对帕累托非支配候选模型使用完整训练集进行微调。我们在PASCAL VOC 2012数据集上搜索能有效权衡精度与计算成本的模型以评估FMAS。FMAS能快速发现具有竞争力的设计，例如仅用0.5 GPU天即可发现一种DeepLabV3+变体，在误差增加不到3%的情况下，将浮点运算次数和参数量分别降低10%和20%。我们还在名为GAP8的边缘设备上进行搜索，以其实时延作为度量标准。FMAS能实现2.2倍加速且仅损失7.61%的平均交并比（MIoU）的网络。