Car-Classification
Goal: Building a vehicle recognition predictive model using machine learning models (traditional and deep learning), and the goal of that model is to classify a car’s make and model based on an input image.
Raw data source: https://www.kaggle.com/jessicali9530/stanford-cars-dataset
Author: Albion Krasniqi
Article - Vehicle Classification: https://medium.com/@albionkrasniqi22_80133/vehicle-classification-742403117f43
Project structure
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ └── raw <- The link to the original data
│ └── names <- csv file with all the names of car classes
│ └── training_labels <- csv file which contains the information about the car in the training set
│ └── testing_labels <- csv file which contains the information about the car in the testing set
│
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ └── data pre-processing
│ └── traditional methods
│ └── deep learning methods
│
├── The combined notebook <- this notebook has everything complied in one place
│
└── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
generated with `pip freeze > requirements.txt`