This code is part of a project developed for the course "Procesamiento de Imágenes Digitales" (Digital Image Processing) at the University of Seville. We trained a ResNet model on an augmented version of the Stanford Cars dataset with Spanish car models.

First, run the following command to install the required dependencies:

pip install -r requirements.txt

To prepare the dataset, run the following command:

python scripts.prepare_dataset

This assumes you have Kaggle API credentials set up. In order to set them up,

1. Create an account on Kaggle if you don't have one (https://www.kaggle.com)
2. Go to your profile → Account → Create New API Token
3. You will download a kaggle.json file
4. Create a directory .kaggle in the project root:

mkdir .kaggle

5. Move the kaggle.json file to the .kaggle directory:

mv /path/to/kaggle.json .kaggle/

To check that the dataset has been prepared correctly, run the following command:

python scripts.check_dataset

You can launch a training session with the following command:

python -m scripts.train --experiment <exp_name>

The details about experiments can be found in lib/experiment.py.

You must have a trained checkpoint to run inference. You can run the following command to start a Streamlit app for inference:

streamlit run app.py -- <resnet_number> <trained_path>

For instance, you can run the following command to start a Streamlit app for inference with ResNet-18:

streamlit run app.py -- 18 ./resnet18.pth

where <resnet_number> is the number of the ResNet model you want to use (currently, only 18 and 50 are supported) and <trained_path> is the path to the trained checkpoint file.