This repository provides a TensorFlow-based suite for semantic image segmentation, enabling users to easily implement, train, and test various state-of-the-art models. It targets researchers and practitioners in computer vision who need a flexible framework for experimenting with different architectures and datasets. The suite offers built-in data augmentation, evaluation metrics, and plotting capabilities, simplifying the workflow for developing custom segmentation solutions.

How It Works

The suite employs a modular design, allowing users to select from a variety of pre-implemented encoder-decoder architectures, including SegNet, UNet, PSPNet, DeepLabV3/V3+, RefineNet, and others. It supports multiple feature extraction backends like MobileNetV2 and ResNet variants. The framework handles data loading, augmentation, training loops, and evaluation, abstracting away much of the boilerplate code typically associated with deep learning projects.

Quick Start & Requirements

• Install: sudo pip install numpy opencv-python tensorflow-gpu
• Prerequisites: Python, NumPy, OpenCV, TensorFlow (GPU recommended).
• Data Setup: Requires a specific folder structure for datasets with train, val, test splits and a class_dict.csv file.
• Pre-trained Weights: Required for models using ResNet backends (PSPNet, RefineNet, DeepLabV3, DeepLabV3+, GCN).
• Docs: https://github.com/GeorgeSeif/Semantic-Segmentation-Suite

Highlighted Details

• Implements 12+ popular semantic segmentation models.
• Supports multiple feature extraction backends (e.g., ResNet, MobileNetV2).
• Includes data augmentation techniques (horizontal/vertical flip, brightness, rotation).
• Provides comprehensive evaluation metrics (precision, recall, F1, mIoU).

Maintenance & Community

The repository is marked as deprecated and will no longer handle issues. Users are encouraged to use it as-is.

Licensing & Compatibility

The README does not explicitly state a license. TensorFlow is typically used with permissive licenses, but specific restrictions are not detailed.

Limitations & Caveats

The project is deprecated and no longer actively maintained, meaning no new issues will be addressed. Some models, like ICNet, are implemented but not yet integrated with the training pipeline. Checkpoint files are not provided due to size limitations.