Abstract
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 15% compared to MobileNetV2. MobileNetV2-Small is 4.6% more accurate while reducing latency by 5% compared to MobileNetV2. MobileNetV3-Large detection is 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.
Abstract (translated)
我们介绍了基于互补搜索技术和新颖的体系结构设计相结合的下一代MobileNet。MobileNetv3通过硬件感知网络体系结构搜索（NAS）与Netadapt算法相结合，调到移动电话CPU，然后通过新的体系结构改进进行改进。本文开始探索自动搜索算法和网络设计如何协同工作，以利用互补的方法来提高整体技术水平。通过这个过程，我们为发布创建了两个新的mobilenet模型：mobilenetv3 large和mobilenetv3 small，它们是针对高和低资源使用情况的。然后将这些模型应用到对象检测和语义分割任务中。针对语义分割（或任何密集像素预测）的任务，我们提出了一种新的高效分割译码器Lite-Reduced Atrus空间金字塔池（LR-ASPP）。我们在移动分类、检测和分割方面取得了新的先进成果。与mobilenetv2相比，mobilenetv3 large在imagenet分类上更精确3.2%，同时将延迟降低了15%。与mobilenetv2相比，mobilenetv2 small的精确度提高了4.6%，同时将延迟降低了5%。mobilenetv3大型检测速度快25%，与mobilenetv2在COCO检测上的精度大致相同。mobilenetv3大型LR-ASPP比mobilenetv2 R-ASPP快30%，在城市景观分割方面具有相似的准确性。
URL
https://arxiv.org/abs/1905.02244
