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.

Last update: Aug 19, 2022

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

GitHub Repository https://sarus.tech/

Scaling and Benchmarking Self-Supervised Visual Representation Learning

FAIR Self-Supervision Benchmark is deprecated. Please see VISSL, a ground-up rewrite of benchmark in PyTorch. FAIR Self-Supervision Benchmark This cod

584 Dec 31, 2022

Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Fast mesh denoising with data driven normal filtering using deep variational autoencoders This is an implementation for the paper entitled "Fast mesh

9 Dec 02, 2022

Implementation of Online Label Smoothing in PyTorch

Online Label Smoothing Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing. Introduction As the abst

83 Dec 14, 2022

Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

Learning to Reconstruct 3D Manhattan Wireframes From a Single Image This repository contains the PyTorch implementation of the paper: Yichao Zhou, Hao

50 Dec 27, 2022

BERT model training impelmentation using 1024 A100 GPUs for MLPerf Training v1.1

Pre-trained checkpoint and bert config json file Location of checkpoint and bert config json file This MLCommons members Google Drive location contain

12 Apr 27, 2022

Banglore House Prediction Using Flask Server (Python)

Banglore House Prediction Using Flask Server (Python) 🌐 Links 🌐 📂 Repo In this repository, I've implemented a Machine Learning-based Bangalore Hous

1 Jan 24, 2022

Cookiecutter PyTorch Lightning

Cookiecutter PyTorch Lightning Instructions # install cookiecutter pip install cookiecutter

8 Nov 06, 2022

Conformer: Local Features Coupling Global Representations for Visual Recognition

Conformer: Local Features Coupling Global Representations for Visual Recognition (arxiv) This repository is built upon DeiT and timm Usage First, inst

378 Jan 08, 2023

This repository contains the implementation of the following paper: Cross-Descriptor Visual Localization and Mapping

Cross-Descriptor Visual Localization and Mapping This repository contains the implementation of the following paper: "Cross-Descriptor Visual Localiza

81 Oct 06, 2022

Uncertain natural language inference

Uncertain Natural Language Inference This repository hosts the code for the following paper: Tongfei Chen*, Zhengping Jiang*, Adam Poliak, Keisuke Sak

14 Sep 01, 2022

Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimization"

Riggable 3D Face Reconstruction via In-Network Optimization Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimizati

130 Jan 02, 2023

Learning where to learn - Gradient sparsity in meta and continual learning

Learning where to learn - Gradient sparsity in meta and continual learning In this paper, we investigate gradient sparsity found by MAML in various co

28 Dec 09, 2022

Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

Few-Shot-Intent-Detection Few-Shot-Intent-Detection is a repository designed for few-shot intent detection with/without Out-of-Scope (OOS) intents. It

73 Dec 26, 2022

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

Overview

Sarus published models

Philosophy

Basic usage

Available models

Owner

Sarus Technologies

Scaling and Benchmarking Self-Supervised Visual Representation Learning

Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Implementation of Online Label Smoothing in PyTorch

Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

BERT model training impelmentation using 1024 A100 GPUs for MLPerf Training v1.1

Banglore House Prediction Using Flask Server (Python)

Cookiecutter PyTorch Lightning

Conformer: Local Features Coupling Global Representations for Visual Recognition

This repository contains the implementation of the following paper: Cross-Descriptor Visual Localization and Mapping

Uncertain natural language inference

Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimization"

Learning where to learn - Gradient sparsity in meta and continual learning

Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

Hashformers is a framework for hashtag segmentation with transformers.

Multi-agent reinforcement learning algorithm and environment

Code for "Intra-hour Photovoltaic Generation Forecasting based on Multi-source Data and Deep Learning Methods."

Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Unofficial implementation of MUSIQ (Multi-Scale Image Quality Transformer)

Migration of Edge-based Distributed Federated Learning