Python Training-a-deep-learning-model-on-the-noisy-CIFAR-dataset Resources

DNA sequence classification by Deep Neural Network: Project Overview worked on the DNA sequence classification problem where the input is the DNA sequ

Convert Pytorch model to onnx or tflite, and the converted model can be visualized by Netron

py-self-organizing-map Simple implementation of Self Organizing Maps (SOMs) with rectangular and hexagonal grid topologies. A SOM is a simple unsuperv

Official code for paper "ISNet: Costless and Implicit Image Segmentation for Deep Classifiers, with Application in COVID-19 Detection". LRPDenseNet.py

TailCalibX : Feature Generation for Long-tail Classification by Rahul Vigneswaran, Marc T. Law, Vineeth N. Balasubramanian, Makarand Tapaswi [arXiv] [

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition Paper: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition accepted fo

JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension (2022, Skelter Labs)

DiffStride is a pooling layer with learnable strides. Unlike strided convolutions, average pooling or max-pooling that require cross-validating stride values at each layer, DiffStride can be initialized with an arbitrary value at each layer (e.g. (2, 2) and during training its strides will be optimized for the task at hand.

This is the official repository of the paper: ElasticFace: Elastic Margin Loss for Deep Face Recognition Paper on arxiv: arxiv Model Log file Pretrain

Differentially private Imagenet training Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)" which introduces a new class of deep generative models that generalizes score-based models to fully nonlinear forward and backward diffusions.

Recommender systems are the systems that are designed to recommend things to the user based on many different factors. The recommender system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest.

An Efficient Library for Training Deep Models This repository provides an efficient PyTorch-based library for training deep models. Installation Make

Hybrid Machine learning Model Clone the Repository Create a new Directory as assests and download the model from the below link Model Link To Start th

APSIPA-TER This code is the implementation of Text Emotion Recognition (TER) with linguistic features. The network model is BERT with a pretrained mod

scdlpicker SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP Objective This is a simple deep learning (DL) repicker module

APSIPA-SER-with-A-and-T This code is the implementation of Speech Emotion Recognition (SER) with acoustic and linguistic features. The network model i

NeuralForecast is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate model benchmarks and SOTA models implemented in PyTorch and PyTorchLightning.

This python library predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language.

VocAug It is difficult to find a script which can augment VOC-format dataset, especially the bbox. Or find a script needs complex requirements so it i

To create a deep learning model which can explain the content of an image in the form of speech through caption generation with attention mechanism on Flickr8K dataset.

This project proposed the deep learning and image processing method to undertake the diagnosis on 2D CT image and 3D CT volume.

Earthquake detection via fiber optic cables using deep learning Author: Fantine Huot Getting started Update the submodules After cloning the repositor

This repository is the official PyTorch implementation of Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

Classification using Deep Learning Requirements PyTorch version = 1.9.1+cu111 Python version = 3.8.10 PyTorch-Lightning version = 1.4.9 Huggingface

This repository contains the official PyTorch implementation of the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015

Welcome to AirSim AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open

Clothing Co-Parsing (CCP) Dataset Clothing Co-Parsing (CCP) dataset is a new clothing database including elaborately annotated clothing items. 2, 098

COVID19-WEB-SCRAPER Open Source Tech Lab - Project [SEMESTER IV] OSTL Assignments OSTL Assignments - 1 OSTL Assignments - 2 Project COVID19 India Data

Amazon Recommendation Review Dataset Analyzer A notebook to analyze Amazon Recommendation Review Dataset. Features Calculates distinct user count, dis

Python inverse kinematics for your robot model based on Pinocchio.

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification The repostiory consists of the code, results and data set links for

This machine learning model was developed for House Prices - Advanced Regression Techniques competition in Kaggle by using several machine learning models such as Random Forest, XGBoost and LightGBM.

🔥 Olympics Data Analysis 🔥 In Data Science field, there is a big topic before creating a model for future prediction is Data Analysis. We can find o

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed model for heart failure prediction accuracy of 88 percent.

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification Introduction. This package includes the pyth

This project is a web porting for NVlabs' StyleGAN2, to facilitate exploring all kinds characteristic of StyleGAN networks. Thanks for NVlabs' excelle

AvatarGAN ✨ Generate Cartoon Images using DC-GAN Deep Convolutional GAN is a generative adversarial network architecture. It uses a couple of guidelin

ML Visual Art Colabs A list of cool Colabs on Machine Learning Imagemaking or other artistic purposes 3D Ken Burns Effect Ken Burns Effect by Manuel R

pixel_character_generator Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included. Dataset TinyHero D

Awesome Machine Learning Jupyter Notebooks for Google Colaboratory A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook

Generative Deep Art A curated list of Generative Deep Art projects, tools, artworks, and models Inbox Get started with making AI art in 2022 – deeplea

The unified machine learning framework, enabling framework-agnostic functions, layers and libraries. Contents Overview In a Nutshell Where Next? Overv

R-YOLOv4 This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detect

ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

pytorch-semseg Semantic Segmentation Algorithms Implemented in PyTorch This repository aims at mirroring popular semantic segmentation architectures i

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b

keras-semantic-segmentation-example Example of semantic segmentation in Keras Single class example: Generated data: random ellipse with random color o

Analysis of a dataset of 10000 passwords to find common trends and mistakes people generally make while setting up a password.

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

Anomaly-detection-Feature-Selection Evaluate on three different ML model for feature selection using Breast cancer data. ML models: SVM, KNN and MLP.

Breast cancer is been classified into benign tumour and malignant tumour. Logistic regression is applied in this model.

Breast Cancer Classification Model is applied on a different dataset

VOODOO Revealing supercooled liquid beyond lidar attenuation Explore the docs » Report Bug · Request Feature Table of Contents About The Project Built

ASL-Classifiers American Sign Language (ASL) is a natural language that serves as the predominant sign language of Deaf communities in the United Stat

Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks

VOS This is the source code accompanying the paper VOS: Learning What You Don’t

Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

News-Recommendation-system-using-Bert4Rec-model Bert4rec for news Recommendation

Deep High Dynamic Range Imaging Benchmark This repository is the pytorch impleme

This project builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techiniques.

Deep learning based model for Cyro ET Sub-tomogram-Detection High degree of stru

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternative libraries that can be used for this purpose, one of which is the PyTorch library.

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

SUMO - Slim U-Net trained on MODA Implementation of the SUMO (Slim U-Net trained on MODA) model as described in: TODO: add reference to paper once ava

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch impleme

Automating reinforcement learning architecture design for code optimization. Che

Data from "Datamodels: Predicting Predictions with Training Data" Here we provid

ADE20k Semantic segmentation with MAE Getting started Install the mmsegmentation

Training-a-deep-learning-model-on-the-noisy-CIFAR-dataset This repository contai

Brain Weight in Humans Variations of head sizes and brain weights in humans Kaggle dataset obtained from this link by Anubhab Swain. Image obtained fr

CovidcasePrediction A machine learning model for Covid case prediction Problem Statement Using regression algorithms we can able to track the active c

neural-modular Simple codebase for flexible neural net training. Allows for seamless exchange of models, dataset, and optimizers. Uses hydra for confi

A transformer that does not hog your GPU memory This is an early in-development codebase: if you want a stable and documented hivemind codebase, look

GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates Vibhor Agarwal, Sagar Joglekar, Anthony P. Young an

Harmonious Textual Layout Generation over Natural Images via Deep Aesthetics Learning Code for the paper Harmonious Textual Layout Generation over Nat

DirectCLR DirectCLR is a simple contrastive learning model for visual representation learning. It does not require a trainable projector as SimCLR. It

SegFormer_Segmentation The code uses SegFormer for Semantic Segmentation on Drone Dataset. The details for the SegFormer can be obtained from the foll

Two-Timescale-DNN Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding This repository contains the entire code for our work

bct_file_generator_for_EasyGSH The aim is to extract timeseries water level 2D information for any designed boundaries within the EasyGSH model domain

Soorgeon Join our community | Newsletter | Contact us | Blog | Website | YouTube Convert monolithic Jupyter notebooks into Ploomber pipelines. soorgeo

This repository contains the raw dataset used in NHNet [1] for the task of News Story Headline Generation. The code of data processing and training is available under Tensorflow Models - NHNet.

Common Voice Utils This repository collects together basic linguistic processing data for using dataset dumps from the Common Voice project. It aims t

Tutorial on Amortized Optimization This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize

Learning Super-Features for Image Retrieval This repository contains the code for running our FIRe model presented in our ICLR'22 paper: @inproceeding

MachineLearning A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts. Tested algorithms:

Nash-MTL Official implementation of "Multi-Task Learning as a Bargaining Game". Setup environment conda create -n nashmtl python=3.9.7 conda activate

Used for data processing in machine learning, and help us to construct ML model more easily from scratch. Can be used in linear model, logistic regression model, and decision tree.

Machine Learning from Scratch Author: Shengxuan Wang From: Oregon State University Content: Building Machine Learning model from Scratch, without usin

streamlit-shap This component provides a wrapper to display SHAP plots in Streamlit.

Official codebase for Can Wikipedia Help Offline Reinforcement Learning?

Contrastive Learning for Neural Topic Model This repository contains the impleme

Thumbor-bootcamp - learning and contribution experience with ❤️ and 🤗 from the thumbor team

MoRecon - A tool for reconstructing missing frames in motion capture data.

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Breast-Cancer-Classification - Using SKLearn breast cancer dataset which contains 569 examples and 32 features classifying has been made with 6 different algorithms

You Only Cut Once (YOCO) YOCO is a simple method/strategy of performing augmenta

A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model (Random Forest Classifier Model ) that helps the user to identify whether someone is showing positive Covid symptoms or not by simply inputting certain values like oxygen level , breath rate , age, Vaccination done or not etc. with the help of kaggle database.

Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score