A Survey on Transformer in CV.

Multi Modal Task Oriented Dialogue System (MMTOD)

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.

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

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

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

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

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

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

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

🤖 Segmentação de faixas de estrada utilizando o Segformer

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

A modular Pytorch Implementation of ViTGAN