Train and Inference on Fashion Mnist dataset using TensorFlowJS

Feed Forward Multi-Layer Neural Network implemented in C++ 17, optimized using OpenMP 5.1 and tested on fashion MNIST dataset.

Fashion Mnist image classification using cross entropy and Triplet loss

There's been much speculation in recent years about neural networks technologies and other deep learning algorithms, primarily because of the popularity of several implementations in the sector utilizing these techniques. Consequently, this hype has yielded several innovative ideas to build open-source libraries and methods to enable the average income tech-savvies to achieve their objective. This research paper aims to examine and illustrate how to use deep learning technologies and algorithms to precisely classify a dataset of fashion images into their respective clothing categories. First, the paper shows the general knowledge of convolutional neural networks (CNN) and the techniques of image classification. Later on, it also discusses the methodology of building a neural network and the simulation process. The results of the neural network simulation are compressively evaluated and discussed.

Explainable AI & fashion talk & experiments

A comprehensive analysis of the Fashion MNIST dataset using PyTorch. Covers data preparation, EDA, baseline modeling, and fine-tuning CNNs like ResNet. Includes modular folders for data, notebooks, and results. Features CSV exports, visualizations, metrics comparison, and a requirements.txt for easy setup. Ideal for ML workflow exploration.

image classification and manipulation in python machine learning on fashion mnist dataset

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Clustering of Fashion MNIST Dataset with Using PCA for dimension reduction and K-means for clustering

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Repository consists of pre-trained CNN model in pytorch, hitting 89% on Fashion MNIST dataset. Adversarial attack was implemented on a given model. Results are below.

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How to perform distributed training on Amazon SageMaker using SageMaker's Distributed Data Parallel library and debug using Amazon SageMaker Debugger.

How to perform training on Amazon SageMaker using SageMaker's script mode and debug using Amazon SageMaker Debugger.

Implementation of the MCNN-14 model for fashion image classification, achieving 93.08% accuracy on Fashion-MNIST. Based on our paper “An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification.”

explains how to build Convolutional Neural Network(CNN) for Fashion MNIST project which is an Image Recognition Clothing Prediction Web App which we are going to build using Convolutional Neural Network and Flask App. This is a complete project tutorial series where we are going to learn how to train the CNN model on Fashion MNIST greyscale Image dataset and build a corresponding Flask based web app to show the Image predictions in terms of Fashion Tags.

Demos for training different CNNs on the Fashion-MNIST dataset

testing Deep Neural Network accuracy on MNIST Clothing dataset

In this work, author trained a CNN classifier using Keras and TensorFlow backend for prediction of fashion-items in Fashion MNIST dataset, achieving an accuracy of 92.5%.

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

Contains courses in specializations of coursera on deep learning

This repository contains code to train and evaluate a neural network model on a subset of the Fashion MNIST dataset using PyTorch. The model achieves remarkable accuracy after 100 epochs of training.

simple CNN achieving 90% accuracy on FashionMNIST dataset

This is the group project for the "Technologies for Multimodal Data Representation and Archives" (TMDRA) exam, I developed this image classsification NN models with my colleagues Viviana Amaro and Alina Jill Simeone

Used DCGANs for generating Synthetic Images

This project applies transfer learning using a pretrained AlexNet model to classify FashionMNIST images. The model was fine-tuned and trained on GPU after necessary preprocessing. It achieved 93.16% accuracy on the test set and 95.87% on the training set.

Repo for Classification problem for FASHION-MNIST dataset

This repository contains a Jupyter Notebook that explores various clustering techniques applied to the Fashion MNIST dataset like K-Means, Hierarchical,etc.

This repository consists of Lab Assignments for course Machine Learning for Data Mining.