MobileNet with CoreML

This is the MobileNet neural network architecture from the paper MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications implemented using Apple's shiny new CoreML framework.

This uses the pretrained weights from shicai/MobileNet-Caffe.

There are two demo apps included:

• Cat Demo. Shows the prediction for a cat picture. Open the project in Xcode 9 and run it on a device with iOS 11 or on the simulator.

• Camera Demo. Runs from a live video feed and performs a prediction as often as it can manage. (You'll need to run this app on a device, it won't work in the simulator.)

  

  
  

• Note: Also check out Forge, my neural net library for iOS 10 that comes with a version of MobileNet implemented in Metal.

• Converting the weights

• The repo already includes a fully-baked MobileNet.mlmodel, so you don't have to follow the steps in this section. However, in case you're curious, here's how I converted the original Caffe model into this .mlmodel file:

• Download the caffemodel file from shicai/MobileNet-Caffe into the top-level folder for this project.

• Note: You don't have to download mobilenet_deploy.prototxt. There's already one included in this repo. (I added a Softmax layer at the end, which is missing from the original.)

• From a Terminal, do the following:

• $ virtualenv -p /usr/bin/python2.7 env
  $ source env/bin/activate
  $ pip install tensorflow
  $ pip install keras==1.2.2
  $ pip install coremltools

• It's important that you set up the virtual environment using /usr/bin/python2.7. If you use another version of Python, the conversion script will crash with Fatal Python error: PyThreadState_Get: no current thread. You also need to use Keras 1.2.2 and not the newer 2.0.

• Run the coreml.py script to do the conversion:

• $ python coreml.py

• This creates the MobileNet.mlmodel file.

• Clean up by deactivating the virtualenv:

• $ deactivate

• Done!