Machine Learning for Guitars - this is an elaboration on tensorflow for poets
pip install virtualenv
virtualenv env
source env/bin/activate
pip install -tensorflow
pip install -pillow
pip install -tfcoreml
IMAGE_SIZE=224
ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"
I used https://github.com/hardikvasa/google-images-download CLI
change the paths in resize.py
cd scripts
python resize.py
python -m scripts.retrain \
--bottleneck_dir=tf_files/bottlenecks \
--how_many_training_steps=500 \
--model_dir=tf_files/models/ \
--summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
--output_graph=tf_files/retrained_graph.pb \
--output_labels=tf_files/retrained_labels.txt \
--architecture="${ARCHITECTURE}" \
--image_dir=tf_files/Guitars
python -m scripts.label_image \
--graph=tf_files/retrained_graph.pb \
--image=../Desktop/sg.jpg
tensorboard --logdir tf_files/training_summaries &
cd scripts
Change all paths to where you want to read/write your model and test your images.
python labelcoreml.py
python converter.py
From Core ML in ArKit, my fork from the presentation will be up soon. https://github.com/hanleyweng/CoreML-in-ARKit
https://github.com/jottenlips/GuitarClassifier-iOS My fork of that repo.
