Digital Stuff Application Development
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Convert a Caffe Model to Core ML Format

Get a tutorial on converting a trained AlexNet Caffe model into a CoreML format that can be used in Apple's devices.

In This Article

Apple's CoreML was first introduced at the WWDC 2017 conference. It brings ML (machine learning) models to Apple's devices using: iOS, iPadOS, watchOS, macOS and tvOS. — i.e without backend support.  

However all models to be used by Apple's devices need to be in CoreML format. Aside from pre-converted models, Apple also provides a coremltools package to convert trained ML models to CoreML models.

Here is an example of converting a Caffee Model to CoreML ML.

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What we need for converting

  1. Python: since coremltools is a python package. Version 3.8.5 is used in this example.
  2. coremltools: version 4.0b2
  3. oxford102.caffemodel
  4. deploy.prototxt
  5. A file contains class labels used in a training model. Class labels map the index of the output of a neural network to labels in a classifier.

For #4 and #5, we will be using these files. 

Step by step

  • Create a folder/directory on a computer: convertmodel. Note: all files will be installed or added to the same folder.
                  cd convertmodel
  • Install coremltools: from a terminal:
                 python -m pip install coremltools==4.0b2 
  • Download oxford102.caffemodel.
  • Download deploy.prototxt
  • Download class_lables.py
  • Generate a file that contains class labels from the trained model. Normally, this file can be provided by the people/team who train the model. In our case, the Github repo used in this example provided a class_labels.py but we need to modify it a bit.
  • Open class_labels.py and add the following line at the end of the file.
        		for label in labels:  
  • From a terminal:
				python class_labels.py > flower_lables.txt

you should see something that looks like this:

  • Create a python script: covert.py. Again, make sure all files used are in the same folder. Open a text editor of your choice. As an important note: generated CoreML model MUST have the extension .mlmodel.
import coremltools 
caffe_modle = ('oxford102.caffemodel', 'deploy.prototxt') 
model_labels = 'class_labels.txt' 
# look into deploy.prototxt file 
# find input: "data" 
image_input = 'data' 
coreml_model = coremltools.converters.caffe.convert(caffe_modle, class_labels=model_labels, 

 Please note: For the image_input field - find this info in deploy.prototxt look for input: 

At this point, we have all info we needed to generate a CoreML model from the terminal:

$> python covert.py 


Finally, you have successfully converted a trained model to a CoreML model!