Semantic Segmentation with MobileNetV2 Encoder and Pix2Pix Decoder

Introduction

Semantic segmentation is a critical task in computer vision, involving the classification of each pixel in an image into predefined categories. This project aims to perform semantic segmentation using a combination of a MobileNetV2 encoder and a Pix2Pix decoder. By leveraging a pre-trained MobileNetV2 model for feature extraction and a Pix2Pix decoder for high-resolution output, the goal is to accurately segment objects in images.

Data

https://drive.google.com/drive/folders/1rHyDNhfFQ7qlXDaS0L4GDRxVWhrPnQOd?usp=sharing

Project Overview

The project consists of several key components:

1. Data Preparation: The dataset consists of images and corresponding masks for training, validation, and testing. Images are resized to a fixed size (e.g., 224x224 pixels), and masks are converted to binary format for semantic segmentation.

2. Model Architecture: The architecture comprises a MobileNetV2 encoder for feature extraction and a Pix2Pix decoder for semantic segmentation. The MobileNetV2 model is used as a feature extractor, and its outputs are passed to the Pix2Pix decoder to generate segmentation masks.

3. Training: The model is trained using the training data, with appropriate loss functions (e.g., Binary Crossentropy) and evaluation metrics (e.g., accuracy, precision, recall). Training progress is monitored, and model checkpoints are saved to ensure the best-performing weights are retained.

4. Validation: The trained model is evaluated on a separate validation dataset to assess its performance and generalization ability. Validation metrics such as accuracy, precision, and recall are calculated to measure model performance.

5. Visualization: Training and validation metrics, including loss and various evaluation metrics, are visualized using plots. Sample predictions are also displayed to showcase the model's segmentation capabilities.

6. Testing: Finally, the trained model is tested on unseen test images to evaluate its performance in real-world scenarios. Test results, including visualizations of predicted masks, are presented to assess the model's effectiveness.

Models

https://drive.google.com/drive/folders/1ocw8LGwHs-o2KSQNF02pdSsF-3s-kXuU?usp=sharing

Conclusion

Semantic segmentation is a challenging yet essential task in computer vision, with applications ranging from object detection and recognition to scene understanding and autonomous navigation. By combining a MobileNetV2 encoder with a Pix2Pix decoder, this project demonstrates a powerful approach to semantic segmentation, achieving accurate and detailed segmentation results on diverse datasets.

The project provides a comprehensive framework for semantic segmentation tasks, offering insights into model architecture, training procedures, evaluation metrics, and visualization techniques. By leveraging state-of-the-art techniques and methodologies, the project contributes to advancing the field of computer vision and paving the way for innovative applications in various domains.