This project demonstrates using the Keras API to classify images from the CIFAR-10 dataset using transfer learning.

Models used the classify our images is created from the pretrained image classifiers:

• VGG16
• EfficientnetV2
• Xception

These networks were originally trained to classify images from the imagenet dataset. This dataset consists of thousands of images divided into 1000 distinct categories. The CIFAR-10 dataset only has 10 classes so we only want 10 output probabilities. We upscale our CIFAR-10 dataset from 32x32x3 -> 224x224x3.

Using Docker. Run by command in data_project directory:

docker compose up -d

And access application with your localhost on 8000 port

Pipenv is a new popular way of automatically creating a 'virtualenv' for the project. It creates Pipfile and Pipfile.lock. Install it by using pip:

pip install pipenv

For the application to work corretly, you need to install the neccessary Python libraries. For the installation of the libraries, use the following command:

pipenv install
pipenv shell

To set up a database connection, enter the necessary settings for connecting to your database in settings.py located in the application folder.

If everything has been installed correctly, run the development server. Use the following command:

$ python manage.py runserver

Open your favorite browser and check at http://127.0.0.1:8000/. You should see a login page that tells you the installation was successfull. Below an example:

Great 🎉 Now to access the function of the application you need to go through authorization.