CV Hackathon Submission

This is our submission to the Aaltoes Computer Vision Hackathon. Our team is [***]. Team participants include [name_1], [name_2], and [name_3]. (Please replace these placeholders with your actual teammate names.)

Overview

In this project, we tackle the challenge of [briefly describe the challenge or problem]. Our solution leverages modern computer vision techniques to preprocess the data, design and train a robust model, and evaluate its performance with clear visualizations and metrics.

Approach

Our solution follows these key steps:

1. Data Preprocessing:
   • Data cleaning, augmentation, and normalization to prepare the dataset.
2. Model Design:
   • Implementation of a model architecture based on [explain architecture, e.g., Convolutional Neural Networks, transfer learning, etc.].
3. Training:
   • Hyperparameter tuning, training with validation, and model optimization.
4. Evaluation:
   • Assessment using metrics such as accuracy, precision, recall, and F1 score, along with visualizations for qualitative analysis.

A fully documented Jupyter Notebook (cv_hackathon.ipynb) walks through our entire workflow, including code explanations, parameter choices, and visualization of results.

Repository Structure

cv_hackathon.ipynb 
/models
    ├── model.py         # Contains model definitions, training scripts, or saved weights  
    └── additional_files # (e.g., pretrained weights, helper scripts, etc.)    
README.md
LICENSE
requirements.txt       # (Optional) Dependencies file