Ziaf

AutoencoderBasedSteganography

Creating-WebMap-with-python-and-folium

Python & Folium

Graphical_user_authentication

LiveTwitterSentimentAnalysis

Twitter live Sentiment Analysis helps us map the positive and the negative sentiments of tweets in real time.

M2m

Neural-Image-Captioning-with-Object-Detection-and-Attention-Mechanism

Image captioning is a task which lies in the intersection of areas of object detection and natural language processing. We will be proposing a, model which will be utilizing both the areas of CV and NLP for the automatic generation of the captions of the given image. Model that we are going to propose mimics the human visual system that automatically describe image content. Main idea of our model is that rather than focusing on the whole image it is better to focus on particular areas like the areas where objects are present in the image. Our model consists of two sub model, first sub model or an encoder consist of object detection part which is used to identify the object in the given image along with their spatial location and finally making annotation vector consist of object features and their spatial feature. Second sub model or decoder consist of RNN based LSTM network along attention network which produce a context vector based on annotation vector at a particular time and finally at each step LSTM takes input of attention network along with the other input to generate caption of a given image. Experimental result on the MSCOCO dataset shows that our model outperforms previous benchmark models.

torchxrayvision

TorchXRayVision: A library of chest X-ray datasets and models.

website-blocker

yolov2

testing module of yolo v2

Ziaf/Neural-Image-Captioning-with-Object-Detection-and-Attention-Mechanism

Image captioning is a task which lies in the intersection of areas of object detection and natural language processing. We will be proposing a, model which will be utilizing both the areas of CV and NLP for the automatic generation of the captions of the given image. Model that we are going to propose mimics the human visual system that automatically describe image content. Main idea of our model is that rather than focusing on the whole image it is better to focus on particular areas like the areas where objects are present in the image. Our model consists of two sub model, first sub model or an encoder consist of object detection part which is used to identify the object in the given image along with their spatial location and finally making annotation vector consist of object features and their spatial feature. Second sub model or decoder consist of RNN based LSTM network along attention network which produce a context vector based on annotation vector at a particular time and finally at each step LSTM takes input of attention network along with the other input to generate caption of a given image. Experimental result on the MSCOCO dataset shows that our model outperforms previous benchmark models.

Ziaf/torchxrayvision

TorchXRayVision: A library of chest X-ray datasets and models.

Ziaf/awesome-gan-for-medical-imaging

Awesome GAN for Medical Imaging

Ziaf/balanced_mixup

Repository for MICCAI 2021 paper Balanced-MixUp for Highly Imbalanced Medical Image Classification

Ziaf/build-basic-generative-adversarial-networks-gans

Notebook 1 : Goal In this notebook, you're going to create your first generative adversarial network (GAN) for this course! Specifically, you will build and train a GAN that can generate hand-written images of digits (0-9). You will be using PyTorch in this specialization, so if you're not familiar with this framework, you may find the PyTorch documentation useful. The hints will also often include links to relevant documentation. Learning Objectives Build the generator and discriminator components of a GAN from scratch. Create generator and discriminator loss functions. Train your GAN and visualize the generated images.

Ziaf/coding-interview-university

A complete computer science study plan to become a software engineer.

Ziaf/DeepLearning

Ziaf/DeTraC_COVId19

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Ziaf/dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Ziaf/google-research

Google Research

Ziaf/HandsOn

Contains the code for different hands on demontrated.

Ziaf/home

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

Ziaf/ImageNet21K

Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper

Ziaf/learn2learn

A PyTorch Library for Meta-learning Research

Ziaf/lime

Lime: Explaining the predictions of any machine learning classifier

Ziaf/M3d-Cam

Ziaf/machine-learning-imbalanced-data

Code repository for the online course Machine Learning with Imbalanced Data

Ziaf/machine_learning_complete

A comprehensive repository containing 30+ notebooks on learning machine learning!

Ziaf/mixup-cifar10

mixup: Beyond Empirical Risk Minimization

Ziaf/mlrgisi

Ziaf/PyTorch-GAN

PyTorch implementations of Generative Adversarial Networks.

Ziaf/pytorch-grad-cam

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Ziaf/SSL4MIS

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

Ziaf/stat453-deep-learning-ss21

STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2021)

Ziaf/TailCalibX

Pytorch implementation of Feature Generation for Long-Tail Classification by Rahul Vigneswaran, Marc T Law, Vineeth N Balasubramaniam and Makarand Tapaswi

Ziaf/torch-cam

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM)

Ziaf/vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Ziaf/web

website

Ziaf/website

Ziaf/xrays-and-gradcam

Classification and Gradient-based Localization of Chest Radiographs using PyTorch.