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Stanford Cars Classification

Description

Image Classification of Car Models using the Stanford Cars Dataset

Installation

Poetry

# install poetry and add poetry to path
curl -sSL https://install.python-poetry.org | python3 -
~/.local/share/pypoetry/venv/bin/poetry

# clone project
git clone https://github.com/marcomoldovan/cars196-classifier
cd cars196-classifier

# install dependencies
poetry install

# activate environment
source $(poetry env info --path)/bin/activate

Pip

# clone project
git clone https://github.com/marcomoldovan/cars196-classifier
cd cars196-classifier

# create virtual environment and activate it
python -m venv .venv
source .venv/bin/activate

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

How to run

Download datasets:

https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset

https://www.kaggle.com/datasets/abdelrahmant11/standford-cars-dataset-meta

Folder structure:

data:

--> stanford-cars-dataset
 
--> stanford-cars-dataset-meta

Train model with default configuration

# train on CPU
python src/train.py trainer=cpu

# train on GPU
python src/train.py trainer=gpu

Train model with chosen experiment configuration from configs/experiment/

python src/train.py experiment=experiment_name.yaml

You can override any parameter from command line like this

python src/train.py trainer.max_epochs=20 data.batch_size=64