Notes taking website build with Docker + Django + React.

Overview

Notes website.

Try it in browser! / But how to run?

Description.

This is monorepository with notes website. Website provides web interface for creating private user notes and working with them. Project uses Python (Django, REST) + React (Boostrap, Router, i18next) + Docker (Compose). This is test task for SimbirSoft (Web/Backend).

API Methods.

See docs/API_METHODS.md or /dev/docs page.

Specification.

See docs/SPECIFICATION.md.

How to run?

See docs/HOW_TO_RUN.md or simply docker-compose up.

Technologies.

See docs/TECHNOLOGIES.md.

Previews.

Home page Login page Notes list page

Efficient Multi Collection Style Transfer Using GAN

Proposed a new model that can make style transfer from single style image, and allow to transfer into multiple different styles in a single model.

Zhaozheng Shen 2 Jan 15, 2022
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models.

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models

AdvBox 1.3k Dec 25, 2022
[NeurIPS 2021] SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

SSUL - Official Pytorch Implementation (NeurIPS 2021) SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning Sun

Clova AI Research 44 Dec 27, 2022
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
DeepGNN is a framework for training machine learning models on large scale graph data.

DeepGNN Overview DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features in

Microsoft 45 Jan 01, 2023
Official repository of "BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment"

BasicVSR_PlusPlus (CVPR 2022) [Paper] [Project Page] [Code] This is the official repository for BasicVSR++. Please feel free to raise issue related to

Kelvin C.K. Chan 227 Jan 01, 2023
CvT2DistilGPT2 is an encoder-to-decoder model that was developed for chest X-ray report generation.

CvT2DistilGPT2 Improving Chest X-Ray Report Generation by Leveraging Warm-Starting This repository houses the implementation of CvT2DistilGPT2 from [1

The Australian e-Health Research Centre 21 Dec 28, 2022
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks Code for “Efficient Sharpness-aware Minimization for Improved Training

Angusdu 32 Oct 18, 2022
DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

DeepConsensus DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS)

Google 149 Dec 19, 2022
Accelerated Multi-Modal MR Imaging with Transformers

Accelerated Multi-Modal MR Imaging with Transformers Dependencies numpy==1.18.5 scikit_image==0.16.2 torchvision==0.8.1 torch==1.7.0 runstats==1.8.0 p

54 Dec 16, 2022
[AAAI 2022] Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

A paper Introduction This is an official release of the paper Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation wit

Jiacheng Wang 14 Dec 08, 2022
Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in "Feature Learning in Infinite-width Neural Networks" This repo contains code to replicate our experiments (Word2Vec, MAML) in

Edward Hu 37 Dec 14, 2022
This codebase proposes modular light python and pytorch implementations of several LiDAR Odometry methods

pyLiDAR-SLAM This codebase proposes modular light python and pytorch implementations of several LiDAR Odometry methods, which can easily be evaluated

Kitware, Inc. 208 Dec 16, 2022
LQM - Improving Object Detection by Estimating Bounding Box Quality Accurately

Improving Object Detection by Estimating Bounding Box Quality Accurately Abstract Object detection aims to locate and classify object instances in ima

IM Lab., POSTECH 0 Sep 28, 2022
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

Torch-template-for-deep-learning Pytorch implementations of some **classical backbone CNNs, data enhancement, torch loss, attention, visualization and

Li Shengyan 270 Dec 31, 2022
Image-to-Image Translation in PyTorch

CycleGAN and pix2pix in PyTorch New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that e

Jun-Yan Zhu 19k Jan 07, 2023
TensorFlow implementation of original paper : https://github.com/hszhao/PSPNet

Keras implementation of PSPNet(caffe) Implemented Architecture of Pyramid Scene Parsing Network in Keras. For the best compability please use Python3.

VladKry 386 Dec 29, 2022
This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task

🌈 ERASOR (RA-L'21 with ICRA Option) Official page of "ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point C

Hyungtae Lim 225 Dec 29, 2022
To prepare an image processing model to classify the type of disaster based on the image dataset

Disaster Classificiation using CNNs bunnysaini/Disaster-Classificiation Goal To prepare an image processing model to classify the type of disaster bas

Bunny Saini 1 Jan 24, 2022
A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.

CCasGNN A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics,

5 Apr 29, 2022