This project is carried out for the purpose of building a machine learning model for classifying images of Hotes from Non-hotels

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

.
├── datasets
│   ├── test_set
│   │   ├── hotels
│   │   └── not-hotels
│   │            
│   │             
│   └── training_set
├       ├── hotels
├       └── not-hotels
├── model
│   └── default_model.h5
│   
│   
├── env
├── utils
├── requirements.txt
├── app.py
└── README.md

Python distribution

Anaconda

Install Anaconda python distribution on your system

If there is an app folder skip this step

python -m venv app

Activate the virtual environment (VENV)

LINUX/Mac: source app/bin/activate

Windows: app\Scripts\activate.bat

Install dependencies in VENV using requirements file

pip3 install -r  env/requirements.txt

Start app (Make sure to enter a valid path to a file or a folder)

Example of valid commands

python app.py predict --path kimono-1986491_640.jpg
python app.py predict --path predict

Make sure you have a dataset folder with the below structure in the root folder of the app

A trained model weight file can be found here, download and put it in the models folder.

Download and extract this file to the root folder of the project or you can train your own model by providing a dataset folder in the root folder of the project with below structure.

.
├── datasets
│   ├── test_set
│   │   ├── valid_case
│   │   └── not_valid_case
│   │            
│   │             
│   └── training_set
├       ├── valid_case
├       └── not_valid_case

python3 train_model.py

• Anaconda - The python distribution used
• VS Code - The IDE used

Read documentation here

This project is licensed under the MIT License - see the LICENSE.md file for details

• Hat tip to anyone whose code was used
• Inspiration
• etc