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---
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
## Features
1. ### Architecture
- Basic **Resnet50** model with few upsampling and batch normalisation layers.
- Contains over **60 million** Trainable paramameters.
- Training Duration: **1 hour**.
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 |