Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Related tags

Deep Learningaitensorflowkerasgenerative-modeltensorflow2
Overview

Sarus published models

Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

The required packages are managed with pipenv and can be installed using pipenv install. Please see the pipenv documentation for more information.

Philosophy

These models' implementations are intended to be easy to read and to adapt by making use of the latest Tensorflow 2 library and Keras API.

Basic usage

To install and train a model.

pipenv install
pipenv shell
python train.py

To visualize losses and reconstructions.

tensorboard --logdir ./logs/

Available models

Owner
Sarus Technologies
Sarus Technologies
GitHub Repository https://sarus.tech/
Workshop Materials Delivered on 28/02/2022

intro-to-cnn-p1 Repo for hosting workshop materials delivered on 28/02/2022 Questions you will answer in this workshop Learning Objectives What are co

Beginners Machine Learning 5 Feb 28, 2022
Exploring Image Deblurring via Blur Kernel Space (CVPR'21)

Exploring Image Deblurring via Encoded Blur Kernel Space About the project We introduce a method to encode the blur operators of an arbitrary dataset

VinAI Research 118 Dec 19, 2022
Optimizes image files by converting them to webp while also updating all references.

About Optimizes images by (re-)saving them as webp. For every file it replaced it automatically updates all references. Works on single files as well

Watermelon Wolverine 18 Dec 23, 2022
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling @ INTERSPEECH 2021 Accepted

NU-Wave — Official PyTorch Implementation NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling Junhyeok Lee, Seungu Han @ MINDsLab Inc

MINDs Lab 242 Dec 23, 2022
STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack This is the official implementation of the paper: STMTrack: Template-free Visual Tracking with Space-time Memory Networks. Setup Prepare Anac

Zhihong Fu 62 Dec 21, 2022
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping.

P-Lambda 437 Dec 30, 2022
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

967 Jan 04, 2023
DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency

[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper) Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang PDF:

Kuang-Jui Hsu 139 Dec 22, 2022
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

English | 简体中文 Latest News 2021.10.25 Paper "Docking-based Virtual Screening with Multi-Task Learning" is accepted by BIBM 2021. 2021.07.29 PaddleHeli

633 Jan 04, 2023
Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

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

Google Research 29 Nov 03, 2022
Si Adek Keras is software VR dangerous object detection.

Si Adek Python Keras Sistem Informasi Deteksi Benda Berbahaya Keras Python. Version 1.0 Developed by Ananda Rauf Maududi. Developed date: 24 November

Ananda Rauf 1 Dec 21, 2021
Advanced Signal Processing Notebooks and Tutorials

Advanced Digital Signal Processing Notebooks and Tutorials Prof. Dr. -Ing. Gerald Schuller Jupyter Notebooks and Videos: Renato Profeta Applied Media

Guitars.AI 115 Dec 13, 2022
nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures.

nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures. Here you will find the scripts necessary to produce th

Jesse Willis 0 Jan 20, 2022
Open Source Differentiable Computer Vision Library for PyTorch

Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer

kornia 7.6k Jan 04, 2023
A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

img_sussifier A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196 Examples How to use install python pip i

41 Sep 30, 2022
PyTorch implementation of SQN based on CloserLook3D's encoder

SQN_pytorch This repo is an implementation of Semantic Query Network (SQN) using CloserLook3D's encoder in Pytorch. For TensorFlow implementation, che

PointCloudYC 1 Oct 21, 2021
This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.

AutoDebias This is the official pytorch implementation of AutoDebias, a debiasing method for recommendation system. AutoDebias is proposed in the pape

Dong Hande 77 Nov 25, 2022
Numenta published papers code and data

Numenta research papers code and data This repository contains reproducible code for selected Numenta papers. It is currently under construction and w

Numenta 293 Jan 06, 2023
Pure python implementation reverse-mode automatic differentiation

MiniGrad A minimal implementation of reverse-mode automatic differentiation (a.k.a. autograd / backpropagation) in pure Python. Inspired by Andrej Kar

Kenny Song 76 Sep 12, 2022
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

octconv.pytorch PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octa

Duo Li 273 Dec 18, 2022