An ML-based age detection app created for the Introduction to ML course at Warsaw University of Technology.
Make sure you have the following installed on your system:
Python 3.11+pip(Python package installer)
git clone <repo url>
cd your_repository
Navigate to the project directory and create a virtual environment:
python -m venv <env_name>
Note: On some systems, you might need to use python3 instead of python
For Windows:
<env_name>\Scripts\activate
For macOS and Linux:
source venv/bin/activate
Your command prompt or terminal should now show the virtual environment name.
Install the required dependencies listed in the requirements.txt file using pip:
pip install -r requirements.txt
Ensure you are still in the activated virtual environment, and execute
the main script to runt the Python application:
python src/main.py
When you are done using the application, deactivate the virtual environment:
deactivate
| Resource Name | Reference |
|---|---|
age_group.png |
created by Freepik - Flaticon, see: https://www.flaticon.com/free-icons/age-group |
You will need to have at least Python 3.11 and Docker installed.
You also need to have Make installed.
If you are on Windows you need to install Chocolatey and run this command: choco install make.
If you just cloned this repository and already performed steps from Initial setup, you may want to
install all dependencies for this project. These dependencies are not required to run the code (as it will be run through Docker),
but to allow full linter support in IDE (you can also use the Docker's version of Python as your interpreter). It is recommended
to install these dependencies in a virtual environment. Once you have a virtual environment
set up, you can install the dependencies by running make install_requirements.
For managing dependencies we are using pip-tools.
All requirements should be added to requirements.in with specified version. Then, using command make add_requirements
we can create requirements.txt which will automatically add all dependencies for packages specified
in requirements.in. After the requirements.txt has been updated we need to install the new dependencies.
To do it, run make install_requirements.
If you just cloned this repository you need to first run make docker_build to build a Docker image.
You also need to run this command whenever the dependencies have changed.
If you want to start Docker container, you need to run: make docker_up.
This will create a Docker container
where you can run your code. To access it, run make enter_docker. With this command you will enter
src folder inside the Docker container (which is an exact copy of this folder on your machine). You can
access all the files and folders that are in the repository folder, they are also dynamically updated in the Docker.
To exit Docker, click Ctrl + D, and then stop the container by make docker_down.
We should store all the data (like images, videos, label files, etc.) inside data
folder, whose content is not synced to GitHub. There are three folders to help
with data organization. All the data should be stored on the Google Drive (if it should be accessible to others)
or localy on the developer's machine.
All the code should be stored in src folder in appropriate subfolder.