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.
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!