Image Segmentation Validation using Keras and OpenCV

This project is focused on validating a pre-trained image segmentation model using Keras and OpenCV. It includes steps for loading the model, normalizing images, and evaluating model performance on validation data. The results are visualized using various plots to analyze the segmentation accuracy and error distribution.

Features

• Model Loading: Use TensorFlow and Keras to load pre-trained models.
• Data Preprocessing: Normalize images before feeding them to the model.
• Evaluation Metrics: Use metrics such as IOU (Intersection Over Union) to assess segmentation quality.
• Visualization: Leverage Matplotlib and Seaborn to visualize segmentation results and performance metrics.

Requirements

• Python 3.x
• Keras
• TensorFlow
• OpenCV
• Matplotlib
• Seaborn
• NumPy
• Pandas

Usage

1. Clone the repository:

   git clone https://github.com/yourusername/segmentation-validation.git
   cd segmentation-validation

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Jupyter notebook:

   jupyter notebook segmentation_A_validation.ipynb

Results

The project generates various metrics and visualizations, including:

• Segmentation masks comparison with ground truth.
• Metrics such as IOU to quantify model performance.
• Visual plots for data and model evaluation.

Acknowledgments

This project utilizes several Python libraries such as Keras, TensorFlow, OpenCV, and Matplotlib, which are instrumental for deep learning and computer vision tasks.