One-Shot Learning with Triplet CNNs in Pytorch

Generative Adversarial Networks in TensorFlow 2.0

Vanilla GAN and WGAN implementations in PyTorch on the FashionMNIST dataset

Fashion Mnist image classification using cross entropy and Triplet loss

A pipeline built on MetaFlow for training Fashion MNIST dataset using Pytorch, experiment tracking using MLFlow and model deployment using BentoML

This repository contains an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) trained on the FashionMNIST dataset. The project aims to generate realistic images of clothing items using a GAN architecture. It includes model definitions, training scripts, and visualizations of generated images at various training stages.

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

classification of fashion data(28 x28 greyscale image) into 10 classes.

A pytorch implementation of Densenet for FashionMNIST dataset

SLIIT 4th Year 2nd Semester Machine Learning Project

This project explores the use of a Generative Adversarial Network (GAN) to generate fashion images from the Fashion MNIST dataset. The generator creates fake images, and the discriminator distinguishes them from real ones. Performance is evaluated using Fréchet Inception Distance (FID) to assess the quality of the generated images.

Hands-on MLOps tooling study: WandB vs MLflow side-by-side using PyTorch + FashionMNIST. Covers experiment tracking, metric logging, model artifacts, and visual analysis. More tools added as studied.

This project uses an Autoencoder for dimension reduction on the Fashion MNIST dataset, which contains grayscale clothing images. The goal is to reduce the 784-dimensional images (28x28) to a 128-dimensional latent space while reconstructing the images. The performance is evaluated using the Structural Similarity Index (SSIM).

Deep Learning Project on Diffusion Models for Image Generation

Implementing DDPM, U-Net, Classifier-Free Guidance, and CLIP-style Cosine Similarity from scratch on FashionMNIST — based on NVIDIA DLI Generative AI with Diffusion Models curriculum. / NVIDIA DLI 'Generative AI with Diffusion Models' 커리큘럼 기반으로 DDPM, U-Net, CFG, CLIP 코사인 유사도를 처음부터 직접 구현한 생성형 AI 포트폴리오.

Une série de notebooks qui expliquent en détail comment fonctionnent les modèles de diffusion

Pytorch implementation of a denoising autoencoder.

A consortium of popular ML algorithms/concepts implemented in Python.

Fashion Image CNN Classifier using Keras