Flower Classifier 🍃

The current repository contains Davide Pollicino' Honours Project. The project (still under evolution), is the result of three research questions:

• What are possible approaches to feature enginnering for the implementation of a classifier able to distinguish flower variants incredibly similar between them, even at differnt life stages and growth locations;
• Which CNN architecture would offer best performances
• Is is possible to integrate a machine learning model within a django App, without that this model would first be deployed in a cloud service and exploses via endpoint?

Technologies

Web application functionalities

The machine learning model, is the the integrated and used in a Django Web APP, where user are able to:

• Classify a flower
• Leave a feedback related to the prediction
• Save a prediction as favourite
• Register, Login, and gets the user's favourite position.

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How to run the project

# create virtual environemnt
python3 -m venv venv

# install project requirements
source venv/bin/activate
pip3 install -r requirements.txt

# run migrations
python3 manage.py makemigrations
python3 manage.py migrate

# create superuser
python3 manage.py creatersuper

# run application
python3 manage.py runserver

(Note: manage.py may be located inside the webappClassifier folder :) )

Coding style checks adopted

• Coding style: Black
• Python Lint: Flake8, flake8-todos
• mypy (for english type checking)

# format folder
black folder_name
# Remote all white spaces from project files
trim .
# Format file to improve syntax in according to flake8 (yes -> 2 times --aggressive)
autopep8 --in-place --aggressive --aggressive filename.py