An off-line judger supporting distributed problem repositories

Related tags

Deep LearningThaw
Overview

Thaw

中文 | English

Thaw is an off-line judger supporting distributed problem repositories. Everyone can use Thaw release problems with license on GitHub or other open sourse repositories, get problems locally and judge programs automatically.

We request people who write problems add licenses to their problems or clear copyright notice so that problems can be shared easier, and data generator and solution as well, which can support generalization test and help improve the problems.

By making it off-line, distributed and based on GitHub, situations in which a few administrators examine a large number of problems can be prevented. The nice atmosphere on GitHub can also make quanlity of discussion higher.

We also hope to improve traditional methods of judging. Making it off-line helps avoid creating motivation of cheating, and we hope people do not exceedingly pursue the optimization of the program, but concentrate on things like the readability of code. We will support judging with less strict limit of time and memory, and estimating the polynomial time complexity of a program.

We will make it simple to configure and highly hackable too.

Installation

Package will be created and be released on pip and GitHub after enough improvement and tests on 0.0.1.

Manual

See ./docs/manual.md.

Also See sampleproblem about how to write a problem with Thaw for example.

Usage

Sorry for the project being incompleted. But it will be completed as quick as possible.

Below are expected result.

Create a problem:

thaw init .
git init sampleproblem
cd sampleproblem
thaw init hello_world
cd hello_world
vim -p hello_world.zh-CN.md hello_world.en-US.md std.cpp std.py checker.py

Solve a problem:

vim hello_world.cpp
thaw submit hello_world.cpp

Release a problem:

git add .
git commit -m "add hello_world"
git remote add origin https://github.com/username/sampleproblem
git push origin master

Download a problem:

git clone https://github.com/username/sampleproblem

How to contribute

Welcome to join us! You can improve Thaw by send an Issue or a Pull Request.

Or create a GitHub repository to release your problem according to the format, and create open and shared atmosphere of algorithm contest with us. You can add the url of your repository to repositories.dat to make it easy for others to get your problems and spread your problems.

We will create a Gitter group when more people join.

Contributors

Thank peers who improve Thaw together and share problems

License

AGPL

Owner
countercurrent_time
countercurrent_time
Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

Realtime Face Anti-Spoofing Detection 🤖 Realtime Face Anti Spoofing Detection with Face Detector to detect real and fake faces Please star this repo

Prem Kumar 86 Aug 03, 2022
Pixel Consensus Voting for Panoptic Segmentation (CVPR 2020)

Implementation for Pixel Consensus Voting (CVPR 2020). This codebase contains the essential ingredients of PCV, including various spatial discretizati

Haochen 23 Oct 25, 2022
Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021

The repo provides the code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2

Yuning Mao 18 May 24, 2022
Pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks."

alpha-GAN Unofficial pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks." arXi

Victor Shepardson 78 Dec 08, 2022
A PyTorch library for Vision Transformers

VFormer A PyTorch library for Vision Transformers Getting Started Read the contributing guidelines in CONTRIBUTING.rst to learn how to start contribut

Society for Artificial Intelligence and Deep Learning 142 Nov 28, 2022
[CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation

RCIL [CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation Chang-Bin Zhang1, Jia-Wen Xiao1, Xialei Liu1, Ying-Cong Chen2

Chang-Bin Zhang 71 Dec 28, 2022
A hybrid SOTA solution of LiDAR panoptic segmentation with C++ implementations of point cloud clustering algorithms. ICCV21, Workshop on Traditional Computer Vision in the Age of Deep Learning

ICCVW21-TradiCV-Survey-of-LiDAR-Cluster Motivation In contrast to popular end-to-end deep learning LiDAR panoptic segmentation solutions, we propose a

YimingZhao 103 Nov 22, 2022
Voice assistant - Voice assistant with python

🌐 Python Voice Assistant 🌵 - User's greeting 🌵 - Writing tasks to todo-list ?

PythonToday 10 Dec 26, 2022
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Official PyTorch Implementation of the paper Simple and Robust Loss Design

Xinyu Huang 28 Oct 27, 2022
RETRO-pytorch - Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch

RETRO - Pytorch (wip) Implementation of RETRO, Deepmind's Retrieval based Attent

Phil Wang 556 Jan 04, 2023
Restricted Boltzmann Machines in Python.

How to Use First, initialize an RBM with the desired number of visible and hidden units. rbm = RBM(num_visible = 6, num_hidden = 2) Next, train the m

Edwin Chen 928 Dec 30, 2022
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

Bogireddy Sai Prasanna Teja Reddy 103 Dec 29, 2022
Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

BERT Got a Date: Introducing Transformers to Temporal Tagging Satya Almasian*, Dennis Aumiller*, and Michael Gertz Heidelberg University Contact us vi

54 Dec 04, 2022
Project ArXiv Citation Network

Project ArXiv Citation Network Overview This project involved the analysis of the ArXiv citation network. Usage The complete code of this project is i

Dennis Núñez-Fernández 5 Oct 20, 2022
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

DongGeun-Yoon 19 Jun 07, 2022
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Benedek Rozemberczki 393 Dec 13, 2022
Learning Correspondence from the Cycle-consistency of Time (CVPR 2019)

TimeCycle Code for Learning Correspondence from the Cycle-consistency of Time (CVPR 2019, Oral). The code is developed based on the PyTorch framework,

Xiaolong Wang 706 Nov 29, 2022
CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches This document describes how to install and use CRISCE (CRItical

Chair of Software Engineering II, Uni Passau 2 Feb 09, 2022
The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational Autoencoders".

Open-KG-canonicalization The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational

International Business Machines 13 Nov 11, 2022
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

DatasetGAN This is the official code and data release for: DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort Yuxuan Zhang*, Huan Li

302 Jan 05, 2023