Introduction
Welcome to this new blog. In this post, we’re going to discuss a new project requirement which is "Enhancing Fashion Image Clarity with Autoencoders". The project aims to enhance fashion image clarity using autoencoders, leveraging the MNIST Fashion Dataset to denoise specific clothing and accessory images through various approaches, including Generative Adversarial Networks (GANs) and Autoencoders.
We'll walk you through the project requirements, highlighting the tasks at hand. Then, in the solution approach section, we'll delve into what we've accomplished, discussing the techniques applied and the steps taken. At last , In the output section, we'll showcase key screenshots of the results obtained from the project.
Let's get started!
Project Requirement
Problem Statement
The dataset is similar to MNIST but includes images of specific clothing and accessory. The objective is to add some noise to the images and then use an Autoencoder to denoise those images.
Dataset Description
● MNIST Fashion Dataset
● Total Images: 70,000
● Train Images: 60,000
● Test Images: 10,000
● Image Size: 28 x 28
● Classes: ‘T-Shirt/top’, ‘Trouser’, ‘Pullover’, ‘Dress’, ‘Coat’, ‘Sandal’, ‘Shirt’, ‘Sneaker’, ‘Bag’, ‘Ankle Boot'
Tasks to be performed
.ipynb file 1
Do you get the same results if you run the notebook multiple times without changing any parameters?
● What happens if we increase the batch_size?
● What other activation functions could we use instead of sigmoid?
● Why is the DCGAN output much better than Vanilla GAN? Elaborate
.ipynb file 2
● Try improving the model and re-code the program from scratch without looking too much at the source code.
● Write-up a summary explaining how your program works
ipynb file 3
As a part of this assignment, you will be performing the following tasks:
● Prepare a detailed python notebook using an Autoencoder to denoise images from MNIST Fashion Dataset with the best accuracy.
● Perform Data Visualization
● Prepare the dataset for the model (add some noise to the images)
● Build an Autoencoder model
● Train the model
● Evaluate trained model performance
● Display the Denoised Images
● Write-up a summary explaining how your program works
Solution approach
In this project, we employed various methods and techniques to accomplish the denoising of fashion images using autoencoders. Here's an overview of the approaches used in each .ipynb file:
.ipynb file 1:
Data Loading and Preprocessing:
● We loaded the Fashion-MNIST dataset, consisting of 70,000 grayscale images of fashion products across 10 categories.
● Images were preprocessed by reshaping and normalizing them to the range [-1, 1].
Model Building:
● Developed a generator and a discriminator for a Generative Adversarial Network (GAN) architecture.
● Utilized convolutional layers for both generator and discriminator to effectively learn and generate realistic images.
● Employed LeakyReLU activation function to avoid sparse gradients.
● Applied BinaryCrossentropy loss function for GAN training.
Training Process:
● Trained the GAN model over 50 epochs, optimizing both the generator and discriminator networks alternatively.
● Implemented gradient tape to compute and apply gradients during training.
● Saved checkpoints for model evaluation and future use.
Results Visualization:
● Generated images at different epochs to observe the progression of image generation quality.
● Created an animated GIF to visualize the training process and generated images over epochs.
.ipynb file 2:
Data Loading and Preprocessing:
● Utilized the Fashion-MNIST dataset, similar to .ipynb file 1.
Model Architecture Definition:
● Defined a GAN model using TensorFlow's Keras API.
● Developed the generator and discriminator networks using convolutional layers for image generation and discrimination.
Training Procedure:
● Trained the GAN model for 50 epochs with batch training.
● Employed Adam optimizer for the generator and RMSprop optimizer for the discriminator.
Results Presentation:
● Visualized reconstructed images to assess the performance of the autoencoder.
● Plotted the Mean Squared Error (MSE) loss over epochs to monitor the training progress.
.ipynb file 3:
Data Preparation:
● Loaded the Fashion-MNIST dataset, similar to the previous files.
Model Construction:
● Designed an Autoencoder model comprising encoder and decoder components.
● Encoder utilized convolutional layers to extract features, followed by a fully connected layer to produce encoded features.
● Decoder employed transposed convolutional layers to reconstruct the images from encoded features.
Training Mechanism:
● Trained the Autoencoder model over 50 epochs using Adam optimizer.
● Calculated the Mean Squared Error (MSE) loss for model evaluation.
Evaluation and Visualization:
● Visualized the denoised images reconstructed by the trained Autoencoder.
● Plotted the MSE loss to analyze the training performance and convergence.
Output
In this project, we explored the potential of autoencoders to improve fashion image clarity, utilizing the MNIST Fashion Dataset. Our experiments with DCGAN and Vanilla GAN models led to notable enhancements in denoising images. Despite facing challenges in model tuning and optimization, these experiences offered profound insights into advanced deep learning techniques, ultimately contributing to the project’s success in achieving high-quality image results.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.