MjdMahasneh/Simple-PyTorch-Semantic-Segmentation-CNNs
📦 Open Source ProjectMjdMahasneh
A collection of essential PyTorch implementations for popular semantic segmentation architectures like UNet and DeepLabv3+.
This repository offers a streamlined approach to building and training semantic segmentation models using PyTorch. By focusing on readability and modularity, it provides developers with a clear understanding of the architectural nuances behind state-of-the-art CNNs. The codebase covers a diverse range of models, including the classic U-Net for biomedical imaging, the atrous convolution-based DeepLabv3+, and the pyramid pooling capabilities of PSPNet. Each implementation is designed to be easily integrated into existing pipelines, making it an excellent resource for those participating in Kaggle competitions or building custom computer vision applications. The project emphasizes simplicity, allowing users to swap between different backbones and architectures with minimal configuration changes, effectively lowering the barrier to entry for advanced image segmentation tasks.
💡Highlights
- ├─Includes UNet, DeepLabv3+, and PSPNet
- ├─Clean, modular PyTorch code
- └─Ideal for rapid prototyping
🎯For
- ├─Computer Vision Engineers
- ├─AI Researchers
- └─Deep Learning Students