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FIRST-MLWorkshop

This is the workshop github for the 'Using Machine Learning to Build an Image Classification App' presented by Neil Deshmukh at FIRST Detroit on April 26th at 1:30 - 3:30 pm.

In this workshop, we will first train a ML algorithm to classify between any image classes that we give it, and then secondly integrate it into an application.

The app integration is optional, but it's a pretty cool direct visualization of the Neural Network in action.

All that you need is:

  • an internet connection (to download the needed materials)
  • (optional) the development environment for your respective phone:
    • Xcode for IOS (only on Mac)
    • Android Studio for Android
    • Cord to connect the phone and computer

Step #0: Setup

Download Python (if you don't already have it)

ONLY Mac Users Developing for IOS:

python2: sudo pip install --upgrade \ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.4.0-py-none-any.whl

python 3: sudo pip3 install --upgrade \ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.4.0-py3-none-any.whl

Download tensorflow following the instructions at https://www.tensorflow.org/install/ (the CPU version will be sufficient for this workshop)

Download google_images_download, if you would like to train the model on the classes that you want it to detect:

pip install google_images_download

Part 1: Developing ML Classification Algorithm:

1. Clone this directory onto your computer:

git clone https://github.com/QuantumTCode/FIRST-MLWorkshop

2. cd into the directory, where all of the code will be run:

cd FIRST-MLWorkshop

3. Download the classes of images that you would like the algorithm to detect:

googleimagesdownload -f jpg -k "[Class 1], [Class 2], [Class 3], [Class 4], [Class 5]"

So an example would be:

googleimagesdownload -f jpg -k "train, car, bus, racecar, motorcycle"

While only 5 classes are downloaded above, you can add more (might decrease accuracy of classification)

Check the images:

python scripts/check_images.py

4. Set up variables for training:

ARCHITECTURE="mobilenet_0.50_224"

tensorboard --logdir tf_files/training_summaries &

If you receive an error stating: TensorBoard attempted to bind to port 6006, but it was already in use kill the existing TensorBoard instance using pkill -f "tensorboard"

5. Time to Initiate Machine Learning!

Start the training of the model using:

python -m scripts.retrain \
  --bottleneck_dir=tf_files/bottlenecks \
  --how_many_training_steps=1000 \
  --model_dir=tf_files/models/ \
  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  --output_graph=tf_files/retrained_graph.pb \
  --output_labels=tf_files/retrained_labels.txt \
  --architecture="${ARCHITECTURE}" \
  --image_dir=downloads

If you have time, and want to increase the accuracy of you model, change --how_many_training_steps=X to a higher number (4000 is probably the highest you need to go).

6. Watch Machine Learning in Action:

To see the real time learning behind the algorithm, go to http://0.0.0.0:6006/ on your computer!

7. Test the Accuracy of Your Algorithm:

python -m scripts.label_image \
    --graph=tf_files/retrained_graph.pb  \
    --image=(path_to_image)

For example:

python -m scripts.label_image \ --graph=tf_files/retrained_graph.pb \ --image=downloads/car/car.png

Part 2: Implementing ML Algorithm on Phone:

1. Setup:

pip install PILLOW

If IOS: sudo gem install cocoapods

2: Optimize model using TensorFlow Lite Optimizing Converter (TOCO):

toco \
  --input_file=tf_files/retrained_graph.pb \
  --output_file=tf_files/optimized_graph.pb \
  --input_format=TENSORFLOW_GRAPHDEF \
  --output_format=TENSORFLOW_GRAPHDEF \
  --input_shape=1,224,224,3 \
  --input_array=input \
  --output_array=final_result \
  --mean_value=128 \
  --std_value=128 \
  --default_range_min=0  \
  --default_ranges_max=6

3: Convert Model to TFLite Format:

toco \
  --input_file=tf_files/retrained_graph.pb \
  --output_file=tf_files/optimized_graph.lite \
  --input_format=TENSORFLOW_GRAPHDEF \
  --output_format=TFLITE \
  --input_shape=1,224,224,3 \
  --input_array=input \
  --output_array=final_result \
  --inference_type=FLOAT \
  --input_type=FLOAT \
  --mean_value=128 \
  --std_value=128 \
  --default_range_min=0  \
  --default_ranges_max=6

4.1: For IOS:

cd ios/camera

Update Pods: pod update

Copy the trained models into the app: cd ../.. cp tf_files/retrained_graph.pb ios/camera/data/retrained_graph.pb

Copy the labels into the app: cp tf_files/retrained_labels.txt ios/camera/data/retrained_labels.txt Open the app: open ios/camera/tf_camera_example.xcworkspace

Make sure to go to Xcode>Preferences and sign in to be able to connect your phone and test the application.

  • Click on the tflite_photos_example file on the left, and name your Bundle Identifier something unique, for example: com.[your name]
  • Click on automatically manage signing

Finally, you should be able to run your app!

4.2: For Android:

Set up Device: https://developer.android.com/studio/debug/dev-options#enable

Copy the labels and models into the app: cp tf_files/optimized_graph.lite android/tflite/app/src/main/assets/graph.lite cp tf_files/retrained_labels.txt android/tflite/app/src/main/assets/labels.txt

Open Android Studio>Open an existing Android Studio project, open FIRST-MLWorkshop/android/tflite, and click 'OK' for Gradle Sync.

Then run the app!