I am following a course on Udemy and now the instructor is converting her Caffe Model into MLModel on Python 2.7 ( I know very outdated). She created this .py file for that
import coremltools
caffe_model = ('oxford102.caffemodel', 'deploy.prototxt')
labels = 'flower-labels.txt'
coreml_model = coremltools.converters.caffe.convert(
caffe_model,
class_labels=labels,
image_input_names='data'
)
coreml_model.save('FlowerClassifier.mlmodel')
Later I realized that it wont work for me because all my versions like Python , Pip are up to date and I realized that I need to use tensor flow for this conversion and also use it in conda environment. I installed miniconda and tensorflow. I have oxford102.caffemodel, deploy.prototxt, flower-labels.txt and convert-script.py files inside a folder named Flower Classification so my question is how can I convert this model into and MLModel as of today?
