Single-Object Semantic Segmentation

This repository is an exercise for the single-object semantic segmentation project on the Oxford-IIIT Pet Dataset. The Oxford-IIIT Pet Dataset is a 37 category pet dataset with roughly 200 images for each class created by the Visual Geometry Group at Oxford. [kaggle.com]

Original dataset was downloaded from https://www.robots.ox.ac.uk/~vgg/data/pets/.

Training Process

In this project, an autoencoder network with thirteen convolutional layers, which has 2,880,643 parameters, is used for semantic segmentation of images with dimensions of 200 x 200 x 3.

To train this network, 5173 images have been used as training data and 2217 images as validation data.

The structure of the model is shown in the image below:

Single-Object Semantic Segmentation Model Structure

This network was trained in the Google colab environment and its final parameters were saved. Its loss curves are shown in the following image:

Usage

Requirements

python 3.9
Tensorflow == 2.11.0
pandas == 1.5.3
numpy == 1.24.2
matplotlib == 3.6.3
keras~=2.11.0
scikit-learn~=1.1.1

from keras.models import load_model
from Utils.data_prepair import stack_data
import matplotlib.pyplot as plt

model = load_model('model_regularization.h5')
prediction = model.predict(image)
image = stack_mask(prediction, image)
plt.imshow(image)

Examples of how this network works are given below:

License