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+# GI Tract Image Segmentation
+
+## Overview
+This project implements a **U-Net deep learning model** for **medical image segmentation**, specifically targeting segmentation of the gastrointestinal (GI) tract from medical scans. The pipeline includes custom data preprocessing, a modular training process, and exportable predictions in **Run-Length Encoding (RLE)** format.
+
+---
+
+## Key Features
+- **Data Pipeline**:
+  - Custom data generator to handle large datasets with RLE-encoded masks.
+  - Dynamic resizing of images and masks to a configurable target size.
+  - Flexible test mode for visualizing individual predictions and ground truths.
+
+- **Model Architecture**:
+  - Based on **TransUNet**, combining convolutional layers with transformer-based features for superior performance.
+  - Option to load pre-trained weights for transfer learning or train from scratch.
+
+- **Evaluation**:
+  - Metrics include **Dice coefficient**, **accuracy**, and **visual analysis** of predictions.
+  - Visualization overlays for ground truths and predictions on original images.
+
+- **Export**:
+  - Saves predictions in RLE format compatible with Kaggle competitions or downstream pipelines.
+
+---
+
+## Requirements
+To set up and run the project, ensure the following dependencies are installed:
+- TensorFlow 2.8+
+- Keras
+- NumPy
+- Pandas
+- OpenCV
+- Matplotlib
+- Scikit-learn
+
+---
+
+## Usage
+
+### **1. Running the Pipeline**
+Run the main script to train, evaluate, or export predictions:
+python GI-Tract-Image-Segmentation.py
+
+---
+
+### **2. Training the Model**
+If training from scratch:
+- Automatically splits the data into training, validation, and test sets.
+- Implements early stopping, learning rate scheduling, and model checkpointing.
+- Saves the best model weights to the `output/` directory.
+
+### **3. Evaluating the Model**
+During evaluation, the script:
+- Computes **Dice coefficient** and loss for each test sample.
+- Visualizes predictions and overlays with ground truths.
+
+---
+
+### **Data Pipeline**
+The project includes a highly modular pipeline:
+- **Custom Generator**:
+  - Decodes RLE masks into binary masks dynamically.
+  - Handles resizing, augmentation, and batch generation.
+- **Training Pipeline**:
+  - Modularized for scalability and customization.
+  - Includes checkpoints and CSV logging of training metrics.
+## Model Architecture
+- **Base Model**: TransUNet
+  - Combines transformer layers for long-range dependency capture with CNNs for spatial feature extraction.
+- **Custom Modifications**:
+  - Configurable input size.
+  - Optional pre-trained weights for transfer learning.