We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Classification||ImageNet||MobileNet-224||Top 1 Accuracy||70.6%||# 14|
|Image Classification||ImageNet||MobileNet-224||Top 5 Accuracy||89.5%||# 14|