metadata

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

Architecture
- Basic Resnet50 model with few upsampling and batch normalisation layers.
- Contains over 60 million Trainable paramameters.
- Training Duration: 1 hour.
Training Data
- Source: (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.
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