An easy and minimal implementation of the Visual Transformer (ViT) in PyTorch, from scratch!

Using Visual Transformers to train a basic image classification model to classify images of lions, tigers, cheetahs, tigers and leopards

A modular Pytorch Implementation of ViTGAN

The repository contains supplementary material to my Master's thesis - Fine-grained Visual Recognition with Side Information

Multi Modal Task Oriented Dialogue System (MMTOD)

Comparison of various deep learning-based medical imaging methods for diagnosing and classifying Alzheimer’s disease at different stages.

A Survey on Transformer in CV.

Simple implementation of CBIR with a pretrained ViT model and local Deeplake DB

Methodology used to classify face images based on unknown criteria as part of a datachallenge organised at Telecom Paris

This repository accompanies our paper Unlocking the Heart Using Adaptive Locked Agnostic Networks and enables replication of the key results.

HydraViT is a PyTorch implementation of the HydraViT model, an adaptive multi-branch transformer for multi-label disease classification from chest X-ray images. The repository provides the necessary code to train and evaluate the HydraViT model on the NIH Chest X-ray dataset.

🤖 Segmentação de faixas de estrada utilizando o Segformer

A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.

Energy Theft Detection using ImageTransformation, DNN, TCN, Transformer, ViT