Image Segmentation Repository

Overview

This repository contains implementations for image segmentation models:

1. U-Net_v1: The original U-Net implementation for semantic segmentation.
2. U-Net_v2: An improved version of U-Net with a better structure and enhanced functionality.
3. FineTune_Deeplabv3: A fine-tuned implementation of the DeepLabv3 model for advanced segmentation tasks.

Structure

/U_NET/
├── U_Net_v1/            # Original U-Net implementation
│   ├── models/          # Model architecture, training, and inference scripts
│   └── data/            # Dataset for training and testing
├── U_Net_v2/            # Improved U-Net implementation
│   ├── scripts/         # Training, inference, and preprocessing scripts
│   └── params/          # Configuration files for training and inference
├── FineTune_Deeplabv3/  # Fine-tuned DeepLabv3 implementation
│   ├── scripts/         # Training and inference scripts
│   └── configs/         # Configuration files for fine-tuning
└── Readme.md            # Repository documentation

Recommendations

• For Basic Semantic Segmentation: Use U_Net_v1 for a straightforward implementation.
• For Improved Segmentation: Use U_Net_v2 for better performance and functionality.
• For Advanced Segmentation: Use FineTune_Deeplabv3 for state-of-the-art results.

Usage

U-Net_v1

• Training: Refer to the models/ folder for training scripts.
• Inference: Use the provided inference scripts in the models/ folder.

U-Net_v2

• Training: Use the train.py script in U_Net_v2/scripts/.
• Inference: Use the inference.py script in U_Net_v2/scripts/.

FineTune_Deeplabv3

• Training: Use the training scripts in FineTune_Deeplabv3/scripts/.
• Inference: Use the inference scripts in FineTune_Deeplabv3/scripts/.

License

[Specify license information]

Acknowledgements

• U-Net architecture: Ronneberger et al., 2015
• DeepLabv3: Chen et al., 2017
• [Any other acknowledgements]