Pizza Classifier - User Manual

This document will walk you through how to setup and use the pizza classification app as well as the deep learning model.

Using the App

These instructions assume that you intend to use VSCode, and that you intend to run the app on your own personal Android device, as I did (as opposed to using a different operating system, like IOS, or an emulator).

Running the Server

• If you haven't already, install Python on your system. Ensure that you select the option to install Pip when it appears. I'm using Python version 3.11.3, but any version 3 or above should work.
• If you haven't already, install VSCode. Upon installing VSCode, open the project folder and install the Python extension.
• In your VSCode terminal, ensure that you're in the top-level project directory (you should be able to see the model, frontend, and backend folders), and run the command pip install -r requirements.txt in your VSCode terminal to install the necessary Python packages.
• Run app.py.
• If using Windows, run ipconfig in the command prompt to find your local IP address, labeled as 'IPV4'. Make note of this, as well as the port app.py is running on.

Installing the App

The specific instructions here would vary by phone model and operating system, so these instructions are intentionally vague and may require additional research for those who aren't tech-savvy.

• Install Flutter, Dart, and Android Studio.
• Add the path to the bin folders in your Flutter and Dart installations to your Path system environment variable.
• Install the Flutter and Dart VSCode extensions.
• Ensure that Flutter is operating correctly by using the flutter doctor command in your VSCode terminal. Disregard issues related to compiling to web or desktop.
• On your Android device, access your developer options menu and enable USB debugging.
• In main.dart, change endpoint variable near the top of the file to reflect the local IP and port number you took note of earlier. It should look like http://<ip>:<port>.
• Connect your phone to your laptop via a USB cable, click 'allow' on your phone, run flutter devices in your VSCode terminal to ensure that Flutter has detected your phone, and run flutter run to compile the app to your phone.

Using the App

The app is relatively simple, and use of it should be fairly intuitive. Upon first launching the app, you'll be greeted with a login screen, where you should either log in or register a new account with a username and password. Once you've successfully logged in to the app, you'll be greeted with a view of your camera, where you can take photos and have our model guess whether your photo contains pizza or not!

The bottom bar has three buttons - The leftmost button opens my YouTube channel, the rightmost button logs the user out, and the middle button takes a photo for prediction.

Using the Model

These instructions assume that you've installed the required Pip packages using the command found in Running the Server.

Setup

Install the package: python -m pip install -e <path to /src>

Usage

Import required packages:

from sandbox import model, layers, activations, costs, predictions

Create the model:

model = model.Model()

Add layers to the model:

model.add(layers.Dense(units=20, activation=activations.ReLU()))
model.add(layers.Dense(units=7, activation=activations.ReLU()))
model.add(layers.Dense(units=5, activation=activations.ReLU()))
model.add(layers.Dense(units=1, activation=activations.Sigmoid()))

Configure the model:

model.configure(learning_rate=0.0075, epochs=2500, cost_type=costs.BinaryCrossentropy())

Train the model:

model.train(train_x, train_y, verbose=True)

Predict with the model:

pred = model.predict(image, prediction_type=predictions.binary_classification)

General Abilities:

• Binary Classification

Activation Functions

• Linear
• Sigmoid
• ReLU
• Tanh
• Heaviside
• Signum
• ELU (Exponential Linear Units)
• SELU (Scaled Exponential Linear Units)
• SLU (Sigmoid Linear Units)
• Softplus
• Softsign
• BentIdentity
• Gaussian
• Arctan
• PiecewiseLinear
• DoubleExponential

Cost Functions

• BinaryCrossentropy
• MSE (Mean Squared Error)
• MAE (Mean Absolute Error)

Layer Types

• Dense (fully connected)
• Dropout