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/**
@page data_import Importing data from existing models
@tableofcontents
@section caffe_data_extractor Extract data from pre-trained caffe model
One can find caffe <a href="https://github.com/BVLC/caffe/wiki/Model-Zoo">pre-trained models</a> on
caffe's official github repository.
The caffe_data_extractor.py provided in the @ref scripts folder is an example script that shows how to
extract the hyperparameter values from a trained model.
@note complex networks might require alter the script to properly work.
@subsection how_to How to use the script
Install caffe following <a href="http://caffe.berkeleyvision.org/installation.html">caffe's document</a>.
Make sure the pycaffe has been added into the PYTHONPATH.
Download the pre-trained caffe model.
Run the caffe_data_extractor.py script by
./caffe_data_extractor.py -m <caffe model> -n <caffe netlist>
For example, to extract the data from pre-trained caffe Alex model to binary file:
./caffe_data_extractor.py -m /path/to/bvlc_alexnet.caffemodel -n /path/to/caffe/models/bvlc_alexnet/deploy.prototxt
The script has been tested under Python2.7.
@subsection result What is the expected ouput from the script
If the script run succesfully, it prints the shapes of each layer onto the standard
output and generates *.dat files containing the weights and biases of each layer.
The @ref arm_compute::utils::load_trained_data shows how one could load
the weights and biases into tensor from the .dat file by the help of Accessor.
*/
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