Deep-features for Recognizing Korean Food

Creating a machine learning model to recognize Korean foods in photos using Google's Inception-v3 Convolutional Neural Network to generate and analyze deep features.

The resulting TensorFlow model was used to develop a Django web server and an Android app that can send photos of Korean food to the web server and receives back the classification label.

Requirements

TensorFlow v6.0
Datsci - my personal library used for data science-related projects
jupyter==1.0.0
matplotlib==1.5.1
MetaMindApi==1.2.2
numpy==1.10.4
pandas==0.17.1
Pillow==3.1.0
prettytable==0.7.2
scikit-learn==0.17.1
scipy==0.17.0
seaborn==0.7.0

Machine Learning

Full report

3,470 photos of 20 Korean dishes were used for this project.

Name	Number of samples
galbijjim	219
kimbab	217
bibimbab	214
hotteok	210
nangmyun	207
dakgalbi	205
sullungtang	193
japchae	193
bulgogi	183
samgyupsal	173
bossam	173
dakbokeumtang	171
jeyookbokkeum	165
samgyetang	158
ddukbokee	150
lagalbi	148
jeon	134
kimchi	127
ramen	118
yookgyejang	112

For transfer learning, TensorFlow was used to retrain the Inception-v3 network's final layer.

$Training Accuracy \label{figure_train_acc}$

$Validation Accuracy \label{figure_valid_acc}$

$Cross Entropy \label{figure_cross_ent}$

$Confusion Matrix on Test Set \label{figure_cm}$

About

Creating a machine learning model to recognize Korean foods in photos using Google's Inception-v3 Convolutional Neural Network to generate and analyze deep features.

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