---
license: creativeml-openrail-m
---
# Deep Learning : Image Segmentation for Autonomous Vehicles

This repository contains the code for the winning model of the Deep Learning Hackathon focused on image segmentation for autonomous vehicles using supervised learning techniques. Our model leverages state-of-the-art deep learning architectures to achieve high accuracy and efficiency in segmenting images critical for autonomous driving.

## Features

- **Advanced Deep Learning Models**: Utilizes advanced architectures like U-Net, SegNet, or DeepLab for precise image segmentation tailored to autonomous vehicle scenarios.
- **Supervised Learning**: Employs labeled datasets for training to ensure accurate segmentation outputs.
- **Data Augmentation**: Implements various data augmentation techniques to enhance model generalization and performance.
- **Custom Loss Functions**: Incorporates custom loss functions to improve segmentation accuracy, particularly in challenging autonomous driving scenarios.
- **Evaluation Metrics**: Includes detailed evaluation metrics such as IoU (Intersection over Union) and Dice coefficient to assess model performance.
- **Visualization Tools**: Provides tools for visualizing segmentation results to facilitate easy interpretation and analysis.

## Dataset

The dataset used for this hackathon includes labeled images from autonomous vehicle environments, ensuring a robust and diverse training set. link to the dataset: https://www.kaggle.com/datasets/bishwashk/iims-hackathon-2024

## Installation

To get started, clone the repository and install the required dependencies:

```bash
git clone https://github.com/yourusername/deeplearning-hackathon-image-segmentation.git
cd deeplearning-hackathon-image-segmentation
pip install -r requirements.txt