https://github.com/muhammadshavaiz/semantic-image-segmentation
This repository implements and evaluates SegNet, U-Net, and models from segmentation_models.pytorch for vehicle segmentation using PyTorch. It showcases these models' capabilities in accurately segmenting vehicles in complex scenes, demonstrating cutting-edge deep learning techniques in computer vision
https://github.com/muhammadshavaiz/semantic-image-segmentation
imagesegmentation pytorch segnet unet
Last synced: 3 months ago
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This repository implements and evaluates SegNet, U-Net, and models from segmentation_models.pytorch for vehicle segmentation using PyTorch. It showcases these models' capabilities in accurately segmenting vehicles in complex scenes, demonstrating cutting-edge deep learning techniques in computer vision
- Host: GitHub
- URL: https://github.com/muhammadshavaiz/semantic-image-segmentation
- Owner: MuhammadShavaiz
- Created: 2024-05-02T18:14:02.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-16T18:52:12.000Z (almost 2 years ago)
- Last Synced: 2025-07-10T09:18:33.294Z (about 1 year ago)
- Topics: imagesegmentation, pytorch, segnet, unet
- Language: Jupyter Notebook
- Homepage:
- Size: 1.41 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Car Segmentation Dataset and Models
This repository contains implementations of various semantic segmentation models applied to a car segmentation dataset. The dataset comprises images of cars with their segmentation masks, allowing for the evaluation of these models' ability to accurately segment cars in images.
## Features
- **Dataset**: Contains images of cars with their segmentation masks.
- **Models**: Implements SegNet, U-Net, and a pre-trained model using `segmentation_models.pytorch`.
- **Evaluation**: Includes metrics for evaluating model performance, such as pixel accuracy and mean IoU.
## Getting Started
### Prerequisites
- Python 3.x
- PyTorch
- `segmentation-models-pytorch`
### Installation
Clone the repository and open in google colab.
### Data Preparation
Download the dataset from the provided link and place it in the appropriate directories (`images` and `masks`)
### Model Implementations
#### SegNet
- Description of SegNet architecture and its relevance to semantic segmentation.
#### U-Net
- Description of U-Net architecture and its relevance to semantic segmentation.
#### Pre-trained Model
- Description of the pre-trained model using `segmentation_models.pytorch` and its benefits.
### Usage
Include usage instructions and code snippets for loading models, preparing data, and performing predictions.
### Evaluation
Explain the evaluation metrics used and how to calculate them using the provided functions.