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Caffe implementation of SSD detection on MobileNetv2, converted from tensorflow.

License: MIT License

Python 57.77% C++ 28.40% Cuda 13.83%

mobilenetv2-ssdlite's Introduction

MobileNetv2-SSDLite

Caffe implementation of SSD detection on MobileNetv2, converted from tensorflow.

Introduction

This is a MobileNetv2-SSDLite model converted from tensorflow, currently only ".prototxt" file is available.

Usage

  1. Firstly you should download the original model from tensorflow.
  2. Use gen_model.py to generate the train.prototxt and deploy.prototxt.
python gen_model.py -s train -l labelmap_coco.prototxt -d trainval_lmdb -c 91 >train.prototxt
python gen_model.py -s deploy -l labelmap_coco.prototxt -d trainval_lmdb -c 91 >deploy.prototxt
  1. Use dump_tensorflow_weights.py to dump the weights of conv layer and batchnorm layer.
  2. Use load_caffe_weights.py to load the dumped weights to deploy.caffemodel.
  3. Use the code in src to accelerate your training if you have a cudnn7, or add "engine: CAFFE" to your depthwise convolution layer to solve the memory issue.

Note

Currently the converted caffemodel is not available. Wait patiently, please.

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