https://github.com/gaiarighetti00/deep-learning-pasta-classification

Deep learning project for pasta image classification using CNNs, data augmentation, and regularization.
https://github.com/gaiarighetti00/deep-learning-pasta-classification

cnn computer-vision deep-learning food-ai image-classification keras tensorflow

Last synced: 3 months ago
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Deep learning project for pasta image classification using CNNs, data augmentation, and regularization.

• Host: GitHub
• URL: https://github.com/gaiarighetti00/deep-learning-pasta-classification
• Owner: GaiaRighetti00
• License: mit
• Created: 2025-09-23T10:19:21.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-09-23T12:02:03.000Z (9 months ago)
• Last Synced: 2025-09-23T13:35:58.969Z (9 months ago)
• Topics: cnn, computer-vision, deep-learning, food-ai, image-classification, keras, tensorflow
• Homepage:
• Size: 7.52 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🍝 Guess the Pasta — Deep Learning with CNNs

![Python](https://img.shields.io/badge/Python-3.10+-blue?logo=python)

![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange?logo=tensorflow)

![Keras](https://img.shields.io/badge/Keras-DeepLearning-red?logo=keras)

![Jupyter](https://img.shields.io/badge/Notebook-Jupyter-orange?logo=jupyter)

![License: MIT](https://img.shields.io/badge/License-MIT-green)

> **Classifying pasta types with Convolutional Neural Networks (CNNs)**  

> Master’s Degree in Data Science — University of Milano-Bicocca (A.Y. 2023/2024)  

---

## 📖 Overview

This project applies **deep learning** to the (delicious) challenge of recognizing pasta varieties from images.  

We start from a small, noisy dataset and iteratively improve the model with **data augmentation**, **regularization**, and **optimizer tuning** to boost generalization and stability.

---

## 🎯 Goals

- Build and train CNNs to classify pasta images.  

- Group images into **short**, **long**, **filled**, **layered** pasta.  

- Mitigate small-dataset issues using augmentation & L2 regularization.  

- Compare optimizers (**RMSprop** vs **Adam**) and report findings.

---

## 📂 Repository Structure

- data/ # (see Data & Model section for download)

- models/pasta_model_M4.4.keras

- notebooks/pasta_classification_notebook.ipynb

- slides_pasta_classification.pdf

- README.md

---

## 🧭 Method & Models

1. **Dataset evolution**

   - Start: 12 classes (~30 img/class) → cleaned duplicates/errors.  

   - Reduced to 10 classes due to look-alike pairs (e.g., tagliatelle vs fettuccine).  

   - Final consolidation into **4 macro-classes**: short, long, filled, layered.

2. **Model iterations**

   - **M1**: Baseline CNN + BatchNorm + Dropout, **RMSprop**.  

   - **M2**: + **Data Augmentation**.  

   - **M3**: + **L2 Regularization**.  

   - **M4**: Switch to **Adam** → *final model saved as* `pasta_model_M4.4.keras`.

3. **Key observations**

   - Validation accuracy rises with augmentation & regularization.  

   - **Long pasta** is hardest (shape changes; sauces/packaging can occlude pasta).  

   - Data quality & size are the main bottlenecks.

---

## Environment

python -m venv .venv

source .venv/bin/activate      # Windows: .venv\Scripts\activate

pip install -r requirements.txt

If requirements.txt is missing, install minimally:

pip install tensorflow keras numpy pandas matplotlib scikit-learn pillow jupyter

---

## Run

Open the notebook and follow the cells:

jupyter notebook notebooks/pasta_classification_notebook.ipynb

To load the pretrained model:

from tensorflow import keras

model = keras.models.load_model("models/pasta_model_M4.4.keras")

---

## 📦 Data & Model (large files)

GitHub’s web upload blocks files >100 MB. We provide large assets as Release attachments.

Download model: models/pasta_model_M4.4.keras

Download datasets (archives): data/raw/pasta_4.zip, pasta_10.zip, pasta_12.zip

How to set up:

Go to Releases → Latest → Assets and download the files.

Place them under the paths shown above (create folders if needed).

---

## 📊 Results (high level)

Initial small dataset → ~0.20 validation accuracy.

Augmentation + L2 reduced overfitting and improved stability.

Misclassifications often occurred on long pasta and occluded images (sauces, packaging).

---

## 🚀 Future Work

Add more high-quality, standardized images per class.

Distinguish raw vs cooked pasta explicitly.

Try transfer learning (ResNet, EfficientNet, MobileNetV3).

Add Grad-CAM visualizations for interpretability.

---

## 📑 Documentation

Slides: docs/slides_pasta_classification.pdf

---

## ⚖️ License

This repository is released under the MIT License.

Data are used for educational purposes; check original sources’ terms before redistribution

✨ From spaghetti to lasagne, let deep learning guess your pasta!