51 Repositories
Python octahedral-distortion-parameters Libraries
Semi-automated OpenVINO benchmark_app with variable parameters
Semi-automated OpenVINO benchmark_app with variable parameters. User can specify multiple options for any parameters in the benchmark_app and the progam runs the benchmark with all combinations of given options.
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.
sam4onnx A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for
Vrcwatch - Supply the local time to VRChat as Avatar Parameters through OSC
English: README-EN.md VRCWatch VRCWatch は、VRChat 内のアバター向けに現在時刻を送信するためのプログラムです。 使
Frbmclust - Clusterize FRB profiles using hierarchical clustering, plot corresponding parameters distributions
frbmclust Getting Started Clusterize FRB profiles using hierarchical clustering,
PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning"
PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".
Torch-mutable-modules - Use in-place and assignment operations on PyTorch module parameters with support for autograd
Torch Mutable Modules Use in-place and assignment operations on PyTorch module p
A Python command-line utility for validating that the outputs of a given Declarative Form Azure Portal UI JSON template map to the input parameters of a given ARM Deployment Template JSON template
A Python command-line utility for validating that the outputs of a given Declarative Form Azure Portal UI JSON template map to the input parameters of a given ARM Deployment Template JSON template
A log likelihood fit for extracting neutrino oscillation parameters
A-log-likelihood-fit-for-extracting-neutrino-oscillation-parameters Minimised the negative log-likelihood fit to extract neutrino oscillation paramete
MM1 and MMC Queue Simulation using python - Results and parameters in excel and csv files
implementation of MM1 and MMC Queue on randomly generated data and evaluate simulation results then compare with analytical results and draw a plot curve for them, simulate some integrals and compare results and run monte carlo algorithm with them
Optimizing synthesizer parameters using gradient approximation
Optimizing synthesizer parameters using gradient approximation NASH 2021 Hackathon! These are some experiments I conducted during NASH 2021, the Neura
Evolving neural network parameters in JAX.
Evolving Neural Networks in JAX This repository holds code displaying techniques for applying evolutionary network training strategies in JAX. Each sc
A Python application that helps users determine their calorie intake, and automatically generates customized weekly meal and workout plans based on metrics computed using their physical parameters
A Python application that helps users determine their calorie intake, and automatically generates customized weekly meal and workout plans based on metrics computed using their physical parameters
FDTD simulator that generates s-parameters from OFF geometry files using a GPU
Emport Overview This repo provides a FDTD (Finite Differences Time Domain) simulator called emport for solving RF circuits. Emport outputs its simulat
Open-source implementation of Google Vizier for hyper parameters tuning
Advisor Introduction Advisor is the hyper parameters tuning system for black box optimization. It is the open-source implementation of Google Vizier w
Png-to-stl - Converts PNG and text to SVG, and then extrudes that based on parameters
have ansible installed locally run ansible-playbook setup_application.yml this sets up directories, installs system packages, and sets up python envir
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models
Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor
Simple program to easily view Euler parameters in 3D.
Simple program to easily view Euler parameters in 3D.
A tool for calculating distortion parameters in coordination complexes.
OctaDist Octahedral distortion calculator: A tool for calculating distortion parameters in coordination complexes. https://octadist.github.io/ Registe
DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time
DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time Introduction This is official implementation for DR-GAN (IEEE TCS
Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, numpy and joblib packages.
Pricefy Car Price Predictor App used to predict the price of the car based on certain input parameters created using python's scikit-learn, fastapi, n
Train custom VR face tracking parameters
Pal Buddy Guy: The anipal's best friend This is a small script to improve upon the tracking capabilities of the Vive Pro Eye and facial tracker. You c
Code for generating Tiktok X-Gorgon, X-Khronos and etc. parameters
TikTok-Algorithm I found this python file from a source which was later deleted. Although the test api functions no longer seem to work, surprisingly
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
Milano (This is a research project, not an official NVIDIA product.) Documentation https://nvidia.github.io/Milano Milano (Machine learning autotuner
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
DeepSpeed+Megatron trained the world's most powerful language model: MT-530B DeepSpeed is hiring, come join us! DeepSpeed is a deep learning optimizat
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning This is the official repository of "Camera Distortion-
Solving SMPL/MANO parameters from keypoint coordinates.
Minimal-IK A simple and naive inverse kinematics solver for MANO hand model, SMPL body model, and SMPL-H body+hand model. Briefly, given joint coordin
MixRNet(Using mixup as regularization and tuning hyper-parameters for ResNets)
MixRNet(Using mixup as regularization and tuning hyper-parameters for ResNets) Using mixup data augmentation as reguliraztion and tuning the hyper par
Time Series Cross-Validation -- an extension for scikit-learn
TSCV: Time Series Cross-Validation This repository is a scikit-learn extension for time series cross-validation. It introduces gaps between the traini
Fast pattern fetcher, Takes a URLs list and outputs the URLs which contains the parameters according to the specified pattern.
Fast Pattern Fetcher (fpf) Coded with 3 by HS Devansh Raghav Fast Pattern Fetcher, Takes a URLs list and outputs the URLs which contains the paramete
Machine Learning approach for quantifying detector distortion fields
DistortionML Machine Learning approach for quantifying detector distortion fields. This project is a feasibility study for training a surrogate model
Framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample resolution
Sample-specific Bayesian Networks A framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample or per-patient re
Distort a video using Seam Carving (video) and Vibrato effect (sound)
Distort videos Applies a Seam Carving algorithm (aka liquid rescale) on every frame of a video, and a vibrato effect on the audio to distort the video
Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).
DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a
This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters.
openmc-plasma-source This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters. The OpenMC sources a
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"
Efficient Neural Architecture Search (ENAS) in PyTorch PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. ENAS red
The tool helps to find hidden parameters that can be vulnerable or can reveal interesting functionality that other hunters miss.
The tool helps to find hidden parameters that can be vulnerable or can reveal interesting functionality that other hunters miss. Greater accuracy is achieved thanks to the line-by-line comparison of pages, comparison of response code and reflections.
Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)
EPSR (Enhanced Perceptual Super-resolution Network) paper This repo provides the test code, pretrained models, and results on benchmark datasets of ou
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.
Automatically create Faiss knn indices with the most optimal similarity search parameters.
It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters
[Unofficial code-base] NeRF--: Neural Radiance Fields Without Known Camera Parameters [ Project | Paper | Official code base ] ⬅️ Thanks the original
Pytorch implementation of "Training a 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet"
Token Labeling: Training an 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet (arxiv) This is a Pytorch implementation of our te
(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters
NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min
Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 B) on a single 16 GB VRAM V100 Google Cloud instance with Huggingface Transformers using DeepSpeed
Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 Billion Parameters) on a single 16 GB VRAM V100 Google Cloud instance with Huggingfa
Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning
The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. I
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin
A Burp extension adding a passive scan check to flag parameters whose name or value may indicate a possible insertion point for SSRF or LFI.
BurpParamFlagger A Burp extension adding a passive scan check to flag parameters whose name or value may indicate a possible insertion point for SSRF
Check for python builtins being used as variables or parameters
Flake8 Builtins plugin Check for python builtins being used as variables or parameters. Imagine some code like this: def max_values(list, list2):
Param: Make your Python code clearer and more reliable by declaring Parameters
Param Param is a library providing Parameters: Python attributes extended to have features such as type and range checking, dynamically generated valu
An implementation of model parallel GPT-3-like models on GPUs, based on the DeepSpeed library. Designed to be able to train models in the hundreds of billions of parameters or larger.
GPT-NeoX An implementation of model parallel GPT-3-like models on GPUs, based on the DeepSpeed library. Designed to be able to train models in the hun
Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning :rocket:
MLJAR Automated Machine Learning Documentation: https://supervised.mljar.com/ Source Code: https://github.com/mljar/mljar-supervised Table of Contents