MobileNetv2

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- benchmark_mobilenetv2.sh
- conv_dw_layer.cpp
- conv_dw_layer.cu
- conv_dw_layer.hpp
- mobilenet_v2_new.caffemodel
- MobileNetV2_deploy.prototxt
- caffe.proto
- dice_coef_loss_layer.cpp
- dice_coef_loss_layer.cu
- dice_coef_loss_layer.hpp
- hello.txt
- resize_layer.cpp
- resize_layer.cu
- resize_layer.hpp
LICENSE
MobileUnet1by2.prototxt
README.md
SmallRefineDet_mobilev2_ssdlite.prototxt

// repository documentation

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# MobileNetv2

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

MobileNetv2

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