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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:

1. 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.)

2. 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.

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

$ python coreml.py

This creates the MobileNet.mlmodel file.

4. Clean up by deactivating the virtualenv:

$ deactivate

Done!