Kaggle challenges - Artificial Neural Networks and Deep Learning

This repository contains projects developed for the Artificial Neural Networks and Deep Learning course at Politecnico di Milano during the academic year 2024-2025.

The repository is organized into two main challenges:

Challenge 1: Image Classification

• Objective: Classify images of red blood cells into different categories based on their morphology.
• Approach: Implemented transfer learning with DenseNet161, fine-tuning the model to optimize classification accuracy.
• Challenges: Dealing with class imbalance, enhancing feature extraction, and optimizing generalization.
• Results: Achieved a high classification accuracy using data augmentation, SMOTE for dataset balancing, and fine-tuning selected layers.

Challenge 2: Image Segmentation

• Objective: Classify Mars surface images into distinct terrain categories.
• Approach: Implemented U-Net with custom architectural modifications (SE blocks, attention mechanisms).
• Challenges: No pre-trained models allowed, requiring training from scratch.
• Results: Achieved competitive segmentation accuracy, optimizing performance through data augmentation and loss function tuning.