Semantic-Segmentation

This repository contains Python code for a semantic segmentation model using TensorFlow. The semantic segmentation model is a deep learning model that can be used to segment images into different classes. The model uses convolutional neural networks (CNNs) to learn the features of the image and then uses a decoder network to generate a segmentation map for each pixel of the input image.

Requirements

• Python 3.6 or later

• TensorFlow 2.0 or later

• NumPy

Files

• model.py: Defines the SemanticSegmentationModel class for the semantic segmentation model.

• train.py: Script to train the semantic segmentation model on training data.

• utils.py: Contains utility functions to load the training and validation data.

Usage

1. Install the required packages by running pip install -r requirements.txt.

2. Prepare your training and validation data as numpy arrays and save them to disk as x_train.npy, y_train.npy, x_val.npy, and y_val.npy.

3. Train the model by running python train.py.

4. The trained model will be saved to a file called model.h5.

Example

Here is an example of how to use the SemanticSegmentationModel class to define and compile the model:

import tensorflow as tf
from model import SemanticSegmentationModel

# Define the model
input_shape = (256, 256, 3)
num_classes = 2
model = SemanticSegmentationModel(input_shape, num_classes)

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Results

License

This project is licensed under the MIT License - see the LICENSE file for details.