SemSeg

Welcome to the didactic repository dedicated to the development and implementation of architectures for semantic segmentation. The primary goal is to provide a learning resource by creating and training lightweight models based on original research papers.

1) Objectives

The main objective of this repository is to implement and train various architectures for semantic segmentation, with a focus on lightweight models to accommodate existing hardware constraints.

2) List of Architectures

Below is a list of architectures that will be implemented. Each architecture is accompanied by a link to the original paper.

• FCN-VGG16 (2014)
• U-Net (2015)
• SegNet (2015)
• ParseNet (2016)
• PSPNet (2017)
• GCN (2017)
• DeepLabV3+ (2018)
• Gated-SCNN (2019)
• BiSeNetV2 (2020)
• STDC-Seg (2021)
• SegFormer (2021)
• PPLiteSeg (2022)
• RTFormer (2022)

3) Setting up the Environment

To configure the environment and start working on this project, follow the steps below:

Requirements

• Python 3.9
• PyTorch 2.1.1

Install Dependencies

pip install -r requirements.txt

Set up the Virtual Environment (optional but recommended)

python -m venv venv
source venv/bin/activate

Follow these steps to ensure you have a properly configured environment for development.