Run the script benchmark_mobilenetv2.sh and you can get the result: top1/top5: 0.7123/0.9018

You can use this model to do a lot of things such as training a smaller mobilenetv2 (By moving params or knowledge distillation)

Small MobileNetV1/V2 is friendly used on a mobile device (U-net for semanic segmentation and refinedet for object detection)

Training details for ImageNet2012 : type: "SGD" lr_policy: "poly" base_lr: 0.045 power: 1 momentum: 0.9 weight_decay: 0.00004

The pytorch version: https://github.com/suzhenghang/MobileNetV2_Pytorch

The prototxt of SmallMobileUnet: https://github.com/suzhenghang/MobileNetv2/blob/master/MobileUnet1by2.prototxt

The prototxt of SmallRefineDet_mobilev2_ssdlite: https://github.com/suzhenghang/MobileNetv2/blob/master/SmallRefineDet_mobilev2_ssdlite.prototxt