edit readme

Author: m15kh

Readme.md

@@ -1,45 +1,51 @@
-# U-Net and AutoEncoder Repository
+# Image Segmentation Repository
 
 ## Overview
 
-This repository contains implementations for:
+This repository contains implementations for image segmentation models:
 
-1. **U-Net**: A semantic segmentation model with training and inference capabilities.
-2. **U-Net_v2**: An improved version of U-Net with a better structure, recommended for use.
-3. **AutoEncoder**: Includes both vanilla and CNN-based autoencoder implementations.
+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/               # Original U-Net implementation
+├── 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
-├── Auto_Encoder/        # AutoEncoder implementations
-│   ├── vanilla_autoencoder/  # Vanilla autoencoder
-│   └── cnn-autoencoder/      # CNN-based autoencoder
+├── FineTune_Deeplabv3/  # Fine-tuned DeepLabv3 implementation
+│   ├── scripts/         # Training and inference scripts
+│   └── configs/         # Configuration files for fine-tuning
 └── Readme.md            # Repository documentation
 ```
 
 ## Recommendations
 
-- **For Semantic Segmentation**: Use `U_Net_v2` as it has a better structure and improved functionality compared to the original `U_Net`.
-- **For AutoEncoding Tasks**: Explore the `Auto_Encoder` folder for vanilla and CNN-based autoencoder implementations.
+- **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/src/`.
-- **Inference**: Use the `inference.py` script in `U_Net_v2/scripts/src/`.
+- **Training**: Use the `train.py` script in `U_Net_v2/scripts/`.
+- **Inference**: Use the `inference.py` script in `U_Net_v2/scripts/`.
 
-### AutoEncoder
+### FineTune_Deeplabv3
 
-- **Vanilla AutoEncoder**: Refer to the `vanilla_autoencoder` folder.
-- **CNN AutoEncoder**: Refer to the `cnn-autoencoder` folder.
+- **Training**: Use the training scripts in `FineTune_Deeplabv3/scripts/`.
+- **Inference**: Use the inference scripts in `FineTune_Deeplabv3/scripts/`.
 
 ## License
 
@@ -48,4 +54,5 @@ This repository contains implementations for:
 ## Acknowledgements
 
 - U-Net architecture: [Ronneberger et al., 2015](https://arxiv.org/abs/1505.04597)
+- DeepLabv3: [Chen et al., 2017](https://arxiv.org/abs/1706.05587)
 - [Any other acknowledgements]