In previous chapter, we discussed how to train an object classifier using our own images. At the end, we got trained model and labels file (retrained_graph.pb, retrained_labels.txt).

In this chapter, We are going to load pre-trained classifer in our own Android app. Unfortunately, we can not use trained model in Android directly. We need to optimize it using a tool, namely "optimize_for_inference", provided by Tensorflow.

1. Build tool "optimize_for_inference"

• Download Tensorflow source code:

  git clone https://github.com/tensorflow/tensorflow.git
  

• Install bazel so that we can use it to build "optimize_for_inference"

  echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
  
  curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
  
  sudo apt-get update && sudo apt-get install bazel
  
  sudo apt-get upgrade bazel
  

• Build "optimize_for_inference"

  cd tensorflow
  
  ./configure # We can choose all default settings
  
  bazel build tensorflow/python/tools:optimize_for_inference # this process takes a while, be patient
  

2. Optimize trained model

Let's assume that our pre-trained model is <folder_path>/retrained_graph.pb. Then, we can use the following command to optimize the model and save it as <folder_path>/retrained_graph_android.pb

bazel-bin/tensorflow/python/tools/optimize_for_inference \
--input=<folder_path>/retrained_graph.pb \
--output=<folder_path>/retrained_graph_android.pb \
--input_names=Mul \
--output_names=final_result

3. Modify Tensorflow Android Demo

• Download Android Demo:

  git clone https://github.com/Nilhcem/tensorflow-classifier-android.git
  

  If we import this project into Android Studio, compile and run, the demo will load a pre-trained classifier which can recognize 1000 classes.
• Load our own model:
  1. Delete the previous ImageNet model from assets/ folder.
  2. Copy our optimized trained model retrained_graph_android.pb and label file retrained_labels.txt into assets/ folder.
  3. Open ClassifierActivity.java and set the following variables:

    private static final int INPUT_SIZE = 299; 
    private static final int IMAGE_MEAN = 128; 
    private static final float IMAGE_STD = 128; 
    private static final String INPUT_NAME = "Mul";
    private static final String OUTPUT_NAME = "final_result";
  

  4. Compile and run. The demo will open the camera and show the confidence score of each corresponding class.

Next: 3. Drone_setup, Previous: 1. How to train an object classifier using our own images, Up: Index