Can-Detect-A-Beer-Can-Classification-Model

Quickstart Guide for Running The Model

Dataset: The raw image dataset and the cropped dataset used by the model can be found on Mega.nz.
Required File: Download and unarchive Beer_Images_Cropped.zip. Ensure the resulting Beer_Images_Cropped folder is in the same directory as the notebooks.

The model is designed to be user-friendly with minimal configurations:

Clone the Repository: Clone the repository to your local machine.
Jupyter Notebooks:
- For brew type classification: Run Multiclass_BrewType_Classification.ipynb.
- For taste labelling: Run Multilabel_Taste_Classification.ipynb.

beer_glossary.csv: Contains information from the raw dataset, including bounding boxes, beer can details, and image filenames from Beer_Images.zip.
UQ_Beer_Repo.xlsx: Provides an overview of each unique beer product in the dataset.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
Experiment_Report.pdf		Experiment_Report.pdf
LICENSE		LICENSE
Multiclass_BrewType_Classification.ipynb		Multiclass_BrewType_Classification.ipynb
Multilabel_Taste_Classification.ipynb		Multilabel_Taste_Classification.ipynb
README.md		README.md
UQ_Beer_Repo.xlsx		UQ_Beer_Repo.xlsx
beer_glossary.csv		beer_glossary.csv