README.md

---
license: apache-2.0
datasets:
- bnsapa/road-detection
base_model:
- microsoft/resnet-50
pipeline_tag: image-segmentation
language:
- en
metrics:
- accuracy
tags:
- deeplearning
- pytorch
- segmentation
- resnet50
---
# Waynet - A Road Segmentation project

## Author
 - **Vishal Adithya.A**

## Overview

This model demonstrates a road segmentation implemented using **deep learning** techniques which predicts the road regions in the input image and returns it in a grayscale format.

## Models
 - **rs1-low.pth**: The lowest performer model with a loss of **0.69%**.
 - **rs1-high.pth**: The highest performer model with a loss of **0.07%**.

## Model Structure
![Screenshot 2025-03-29 at 5.49.40 PM.png](https://cdn-uploads.huggingface.co/production/uploads/6787e9bb4151553bf9307186/dCLpzMaW7tZpbn2jJci5_.png)

## Features
1. ### Architecture
    - Basic **Resnet50** model with few upsampling and batch normalisation layers.
    - Contains over **60 million** Trainable paramameters.
    - Training Duration: **1 hour**.
     ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6787e9bb4151553bf9307186/rdnXew3tWUVGoKXhRK8SX.png)
2. ### Training Data
    - Source: ([bnsapa/road-detection](https://huggingface.co/datasets/bnsapa/road-detection))
    - Format: The dataset includes RGB images of roads around the globe and their corresponding segment and lane.
    - Preprocessing: With the help of torch and torchvission api basic preprocessing like resizing and convertion to tensor were implemented.
3. ### CostFunctions Score
    - BCE: **0.07**
    - MSE: **nil**
    - [**NOTE**: All the above scores are trained using the highest performer model]
## License
This project is licensed under the **Apache License 2.0**.

## Acknowledgments
 - **Apple M1 Pro 16gb** of unified memory for efficient GPU acceleration during model training
 - **Pytorch** for robust deep learning framework