YOLO with Core ML and MPSNNGraph
This is the source code for my blog post YOLO: Core ML versus MPSNNGraph.
YOLO is an object detection network. It can detect multiple objects in an image and puts bounding boxes around these objects. Read my other blog post about YOLO to learn more about how it works.
Previously, I implemented YOLO in Metal using the Forge library. Since then Apple released Core ML and MPSNNGraph as part of the iOS 11 beta. So I figured, why not try to get YOLO running on these two other technology stacks too?
In this repo you'll find:
- TinyYOLO-CoreML: A demo app that runs the Tiny YOLO neural network on Core ML.
- TinyYOLO-NNGraph: The same demo app but this time it uses the lower-level graph API from Metal Performance Shaders.
- Convert: The scripts needed to convert the original DarkNet YOLO model to Core ML and MPS format.
To run the app, just open the xcodeproj file in Xcode 9 and run it on a device with iOS 11 or better installed.
The reported "elapsed" time is how long it takes the YOLO neural net to process a single image. The FPS is the actual throughput achieved by the app.
NOTE: Running these kinds of neural networks eats up a lot of battery power. To measure the maximum speed of the model, the
setUpCamera()method in ViewController.swift configures the camera to run at 240 FPS, if available. In a real app, you'd use at most 30 FPS and possibly limit the number of times per second it runs the neural net to 15 or less (i.e. only process every other frame).
NOTE: As of iOS 12, Vision has built-in support for YOLO models. The big advantage is that these do the bounding box decoding and NMS inside the Core ML model, so all you need to do is pass in the image, and Vision will give you the results as one or more
VNRecognizedObjectObservationobjects. No more messing around with
MLMultiArrays. It's also really easy to train such models using Turi Create.
Converting the models
NOTE: You don't need to convert the models yourself. Everything you need to run the demo apps is included in the Xcode projects already.
If you're interested in how the conversion was done, there are three conversion scripts:
The original network is in Darknet format. I used YAD2K to convert this to Keras. Since coremltools currently requires Keras 1.2.2, the included YAD2K source code is actually a modified version that runs on Keras 1.2.2 instead of 2.0.
First, set up a virtualenv with Python 3:
virtualenv -p /usr/local/bin/python3 yad2kenv source yad2kenv/bin/activate pip3 install tensorflow pip3 install keras==1.2.2 pip3 install h5py pip3 install pydot-ng pip3 install pillow brew install graphviz
Run the yad2k.py script to convert the Darknet model to Keras:
cd Convert/yad2k python3 yad2k.py -p ../tiny-yolo-voc.cfg ../tiny-yolo-voc.weights model_data/tiny-yolo-voc.h5
To test the model actually works:
python3 test_yolo.py model_data/tiny-yolo-voc.h5 -a model_data/tiny-yolo-voc_anchors.txt -c model_data/pascal_classes.txt
This places some images with the computed bounding boxes in the
The coreml.py script takes the
tiny-yolo-voc.h5 model created by YAD2K and converts it to
TinyYOLO.mlmodel. Note: this script requires Python 2.7 from
/usr/bin/python (i.e. the one that comes with macOS).
To set up the virtual environment:
virtualenv -p /usr/bin/python2.7 coreml source coreml/bin/activate pip install tensorflow pip install keras==1.2.2 pip install h5py pip install coremltools
coreml.py script to do the conversion (the paths to the model file and the output folder are hardcoded in the script):
The nngraph.py script takes the
tiny-yolo-voc.h5 model created by YAD2K and converts it to weights files used by
MPSNNGraph. Requires Python 3 and Keras 1.2.2.