Final project for Data Science bootcamp organized by Sages

Important:

To run this project it is necessary to download dataset from Stanford University page - https://ai.stanford.edu/~jkrause/cars/car_dataset.html.

After downloading tar file of all images (section Download -> Update), please unpack all images and store them in dataset_original/car_ims so the directory looks as follows:

- dataset_original/
    -car_ims/
        *all images should be stored here*
    - cars_annos.mat

cars_annos.mat file is already provided in this repository.

Please use Python 3.9 or higher version. Install libaries from requirements.txt

The project goal is to classify a brand of car based on its image. To build a model, the transfer learning method will be used. Pretrained models that are taken into consideration in this project are as follows:

• ResNet50
• InceptionV3
• MobileNetV2
• VGG19

Moreover, some necessary changes will be applied to top layers of pretrained models to match the requirements of specific dataset. Later on, fine-tuning will be used, to improve models that have the most potential.

Example of prediction on a sample