chaitanya-ramji/See-Food

A CoreML model which classifies food on the basis of photos and gives the nutritive content of the same.

Description

An iOS Application inspired by HBO's Silicon Valley built to classify food and provide it's nutritive information. The app contains a CoreML model built with Keras 1.2.2 and is a fine-tuned InceptionV3 model. The model is built to recognize over 150 dishes from multiple cuisines. For each of these dishes I gathered around 800 to 900 images from Google, Instagram and Flickr which matched a search query to the title of the category. I was able to achieve 86.97% Top-1 Accuracy and 97.42% Top-5 Accuracy

Introduction

Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. determining whether a picture is that of a dog or cat.

You don't have to limit yourself to a binary classifier of course; CNNs can easily scale to thousands of different classes, as seen in the well-known ImageNet dataset of 1000 classes, used to benchmark computer vision algorithm performance.

The Project

As an introductory project for myself, and on the lines of what I have learnt in CS229 at Stanford, I chose to use a pre-trained image classifier that comes with Keras. I have been in love with HBO's silicon valley and I'm especially fond of the character Jian Yang. Hence I chose this project.

There are 150 different classes of food with 800 to 900 labeled images per class for supervised training.

Approach

I found this script on github: keras-finetuning and used it for the finetuning of my dataset.

Over the last 2 months, I have built this project by exploring various models for machine learning. This is the best result I have achieved as of October 7th 2017

Once I built my model, I had to convert it into a .mlmodel file for use inside Xcode.

Converting Trained Models to Core ML

I used Core ML Tools to convert it to the Core ML model format.

NOTE

Core ML Tools is a Python package (coremltools), hosted at the Python Package Index (PyPI). For information about Python packages, see Python Packaging User Guide.

This is how you can convert a kera built model to CoreML using the tools.

import coremltools
coreml_model = coremltools.converters.keras.convert('SeeFood.h5')

coreml_model.save('SeeFood.mlmodel')

Once the CoreML model was created, I imported it into my Xcode workspace and let Xcode automatically create the class for it. I simply used these classes in my swift code for detection of food.

Code and Usage

1. Download the .mlmodel file and drag it into you Xcode Project and wait for a moment.

2. You will see that Xcode has generated input and output classes, and the main class SeeFood, which has a model property and two prediction methods.

3. Open ViewController.swift and import CoreML

import UIKit
import CoreML

4. Implement a method to either capture a photo using AVFoundation or using a photo in your library using Photos framework.

5. Implement the required methods for image classification.

let model = SeeFood()

let previewImage = UIImage(data: imageData)

guard let input = model.preprocess(image: previewImage!) else {
            print("preprocessing failed")
            return
}
        
guard let result = try? model.prediction(image: input) else {
            print("prediction failed")
            return
}

let confidence = result.foodConfidence["\(result.classLabel)"]! * 100.0

print("\(result.classLabel) - \(converted) %")

Screenshots

Future Plans

• Cuisine Categorization
• Fetch Recipe for image
• Increase dataset to include over 300 titles
• Reduce latency while identifying the food item
• Port app to Android/Web