817 Repositories
Python document-classification Libraries
One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing".
Introduction One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing". Users
Styled Handwritten Text Generation with Transformers (ICCV 21)
⚡ Handwriting Transformers [PDF] Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan & Mubarak Shah Abstract: We
Web Scraping, Document Deduplication & GPT-2 Fine-tuning with a newly created scam dataset.
Web Scraping, Document Deduplication & GPT-2 Fine-tuning with a newly created scam dataset.
Weakly-supervised Text Classification Based on Keyword Graph
Weakly-supervised Text Classification Based on Keyword Graph How to run? Download data Our dataset follows previous works. For long texts, we follow C
PyTorch implementation of our method for adversarial attacks and defenses in hyperspectral image classification.
Self-Attention Context Network for Hyperspectral Image Classification PyTorch implementation of our method for adversarial attacks and defenses in hyp
CONditionals for Ordinal Regression and classification in tensorflow
Condor Ordinal regression in Tensorflow Keras Tensorflow Keras implementation of CONDOR Ordinal Regression (aka ordinal classification) by Garrett Jen
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021. Bobo Xi, Jiaojiao Li, Yunsong Li and Qian Du. Code f
CONditionals for Ordinal Regression and classification in PyTorch
CONDOR pytorch implementation for ordinal regression with deep neural networks. Documentation: https://GarrettJenkinson.github.io/condor_pytorch About
This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Con
Codebase for Image Classification Research, written in PyTorch.
pycls pycls is an image classification codebase, written in PyTorch. It was originally developed for the On Network Design Spaces for Visual Recogniti
Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"
This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and
Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification
next_best_view_rl Setup Clone the repository: git clone --recurse-submodules ... In 'third_party/zed-ros-wrapper': git checkout devel Install mujoco `
This is an official pytorch implementation of Fast Fourier Convolution.
Fast Fourier Convolution (FFC) for Image Classification This is the official code of Fast Fourier Convolution for image classification on ImageNet. Ma
Perform Linear Classification with Multi-way Data
MultiwayClassification This is an R package to perform linear classification for data with multi-way structure. The distance-weighted discrimination (
A cross-document event and entity coreference resolution system, trained and evaluated on the ECB+ corpus.
A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference Resolution. Introduction This repo contains experimental code derived from
Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems.
CottonWeeds Deep learning models for classification of 15 common weeds in the southern U.S. cotton production systems. requirements pytorch torchsumma
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision
Permute Me Softly: Learning Soft Permutations for Graph Representations
Permute Me Softly: Learning Soft Permutations for Graph Representations
Docbarcodes extracts 1D and 2D barcodes from scanned PDF documents or images. It can be used to automate extraction and processing of all kind of documents.
Intro Barcodes are being used in many documents or forms to enable machine reading capabilities and reduce manual processing effort. Simple 1D barcode
classification task on dataset-CIFAR10,by using Tensorflow/keras
CIFAR10-Tensorflow classification task on dataset-CIFAR10,by using Tensorflow/keras 在这一个库中,我使用Tensorflow与keras框架搭建了几个卷积神经网络模型,针对CIFAR10数据集进行了训练与测试。分别使
Code for classifying international patents based on the text of their titles/abstracts
Patent Classification Goal: To train a machine learning classifier that can automatically classify international patents downloaded from the WIPO webs
a basic code repository for basic task in CV(classification,detection,segmentation)
basic_cv a basic code repository for basic task in CV(classification,detection,segmentation,tracking) classification generate dataset train predict de
Tribuo - A Java machine learning library
Tribuo - A Java prediction library (v4.1) Tribuo is a machine learning library in Java that provides multi-class classification, regression, clusterin
Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.
Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression
Document Web APIs made with Django Rest Framework
DRF Docs Document Web APIs made with Django Rest Framework. View Demo Contributors Wanted: Do you like this project? Using it? Let's make it better! S
Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.
Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.
Train 🤗-transformers model with Poutyne.
poutyne-transformers Train 🤗 -transformers models with Poutyne. Installation pip install poutyne-transformers Example import torch from transformers
using Machine Learning Algorithm to classification AppleStore application
AppleStore-classification-with-Machine-learning-Algo- using Machine Learning Algorithm to classification AppleStore application. the first step : 1: p
PyTorch implementation of Weak-shot Fine-grained Classification via Similarity Transfer
SimTrans-Weak-Shot-Classification This repository contains the official PyTorch implementation of the following paper: Weak-shot Fine-grained Classifi
Automatically resolve RidderMaster based on TensorFlow & OpenCV
AutoRiddleMaster Automatically resolve RidderMaster based on TensorFlow & OpenCV 基于 TensorFlow 和 OpenCV 实现的全自动化解御迷士小马谜题 Demo How to use Deploy the ser
Differentiable architecture search for convolutional and recurrent networks
Differentiable Architecture Search Code accompanying the paper DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arX
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
Imbalanced Dataset Sampler Introduction In many machine learning applications, we often come across datasets where some types of data may be seen more
A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
PyTorch Project Template is being sponsored by the following tool; please help to support us by taking a look and signing up to a free trial PyTorch P
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
Neural-Backed Decision Trees · Site · Paper · Blog · Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah
This project deploys a yolo fastest model in the form of tflite on raspberry 3b+. The model is from another repository of mine called -Trash-Classification-Car
Deploy-yolo-fastest-tflite-on-raspberry 觉得有用的话可以顺手点个star嗷 这个项目将垃圾分类小车中的tflite模型移植到了树莓派3b+上面。 该项目主要是为了记录在树莓派部署yolo fastest tflite的流程 (之后有时间会尝试用C++部署来提升
Mayan EDMS is a document management system.
Mayan EDMS is a document management system. Its main purpose is to store, introspect, and categorize files, with a strong emphasis on preserving the contextual and business information of documents. It can also OCR, preview, label, sign, send, and receive thoses files.
Scripts for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation and a convolutional neural network (CNN) for image classification
About subwAI subwAI - a project for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation
docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
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
A CRM department in a local bank works on classify their lost customers with their past datas. So they want predict with these method that average loss balance and passive duration for future.
Rule-Based-Classification-in-a-Banking-Case. A CRM department in a local bank works on classify their lost customers with their past datas. So they wa
A Structured Self-attentive Sentence Embedding
Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR
Library for fast text representation and classification.
fastText fastText is a library for efficient learning of word representations and sentence classification. Table of contents Resources Models Suppleme
CNNs for Sentence Classification in PyTorch
Introduction This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch. Kim's implementation of t
Official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
CrossViT This repository is the official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification. ArXiv If
Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer This repository contains the PyTorch code for Evo-ViT. This work proposes a slow-fas
The project is investigating methods to extract human-marked data from document forms such as surveys and tests.
The project is investigating methods to extract human-marked data from document forms such as surveys and tests. They can read questions, multiple-choice exam papers, and grade.
A collection of SOTA Image Classification Models in PyTorch
A collection of SOTA Image Classification Models in PyTorch
CDLA: A Chinese document layout analysis (CDLA) dataset
CDLA: A Chinese document layout analysis (CDLA) dataset 介绍 CDLA是一个中文文档版面分析数据集,面向中文文献类(论文)场景。包含以下10个label: 正文 标题 图片 图片标题 表格 表格标题 页眉 页脚 注释 公式 Text Title
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
NLPIR tutorial: pretrain for IR. pre-train on raw textual corpus, fine-tune on MS MARCO Document Ranking
pretrain4ir_tutorial NLPIR tutorial: pretrain for IR. pre-train on raw textual corpus, fine-tune on MS MARCO Document Ranking 用作NLPIR实验室, Pre-training
DataCLUE: 国内首个以数据为中心的AI测评(含模型分析报告)
DataCLUE 以数据为中心的AI测评(DataCLUE) DataCLUE: A Chinese Data-centric Language Evaluation Benchmark 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE)的背景 任务描述 任务描述 实验结果
GBIM(Gesture-Based Interaction map)
手势交互地图 GBIM(Gesture-Based Interaction map),基于视觉深度神经网络的交互地图,通过电脑摄像头观察使用者的手势变化,进而控制地图进行简单的交互。网络使用PaddleX提供的轻量级模型PPYOLO Tiny以及MobileNet V3 small,使得整个模型大小约10MB左右,即使在CPU下也能快速定位和识别手势。
UniLM AI - Large-scale Self-supervised Pre-training across Tasks, Languages, and Modalities
Pre-trained (foundation) models across tasks (understanding, generation and translation), languages (100+ languages), and modalities (language, image, audio, vision + language, audio + language, etc.)
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"
This repository contains the official release of the model "BanglaBERT" and associated downstream finetuning code and datasets introduced in the paper titled "BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding".
BanglaBERT This repository contains the official release of the model "BanglaBERT" and associated downstream finetuning code and datasets introduced i
🍊 PAUSE (Positive and Annealed Unlabeled Sentence Embedding), accepted by EMNLP'2021 🌴
PAUSE: Positive and Annealed Unlabeled Sentence Embedding Sentence embedding refers to a set of effective and versatile techniques for converting raw
Document processing using transformers
Doc Transformers Document processing using transformers. This is still in developmental phase, currently supports only extraction of form data i.e (ke
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"
Text-AutoAugment (TAA) This repository contains the code for our paper Text AutoAugment: Learning Compositional Augmentation Policy for Text Classific
The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"
The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications.
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications.
Classifying audio using Wavelet transform and deep learning
Audio Classification using Wavelet Transform and Deep Learning A step-by-step tutorial to classify audio signals using continuous wavelet transform (C
This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Technique for Text Classification
The baseline code is for EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks
Reading list for research topics in sound event detection
Sound event detection aims at processing the continuous acoustic signal and converting it into symbolic descriptions of the corresponding sound events present at the auditory scene.
CTRL-C: Camera calibration TRansformer with Line-Classification
CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca
Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021
Deep Representation One-class Classification (DROC). This is not an officially supported Google product. Tensorflow 2 implementation of the paper: Lea
multi-label,classifier,text classification,多标签文本分类,文本分类,BERT,ALBERT,multi-label-classification,seq2seq,attention,beam search
multi-label,classifier,text classification,多标签文本分类,文本分类,BERT,ALBERT,multi-label-classification,seq2seq,attention,beam search
Prototype for Baby Action Detection and Classification
Baby Action Detection Table of Contents About Install Run Predictions Demo About An attempt to harness the power of Deep Learning to come up with a so
A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.
About This repository provides data and code for the paper: Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development (subm
code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa
Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.
Pytorch-NLU,一个中文文本分类、序列标注工具包,支持中文长文本、短文本的多类、多标签分类任务,支持中文命名实体识别、词性标注、分词等序列标注任务。 Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.
machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service
This is a machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service. We initially made this project as a requirement for an internship at Indian Servers. We are now making it open to contribution.
Mail classification with tensorflow and MS Exchange Server (ham or spam).
Mail classification with tensorflow and MS Exchange Server (ham or spam).
Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)
🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)
PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
PaddlePaddle Vision Transformers State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 🤖 PaddlePaddle Visual Transformers (PaddleViT or
An original implementation of "Noisy Channel Language Model Prompting for Few-Shot Text Classification"
Channel LM Prompting (and beyond) This includes an original implementation of Sewon Min, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer. "Noisy Cha
(ICCV'21) Official PyTorch implementation of Relational Embedding for Few-Shot Classification
Relational Embedding for Few-Shot Classification (ICCV 2021) Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho [paper], [project hompage] We propose t
【ACMMM 2021】DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning (ACMMM 2021) Overview We release the code of the DSANet (Dynamic S
DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task
DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task。涵盖68个领域、共计916万词的专业词典知识库,可用于文本分类、知识增强、领域词汇库扩充等自然语言处理应用。
Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch
disclaimer: this code is modified from pytorch-tutorial Image classification with synthetic gradient in Pytorch I implement the Decoupled Neural Inter
Pipeline for fast building text classification TF-IDF + LogReg baselines.
Text Classification Baseline Pipeline for fast building text classification TF-IDF + LogReg baselines. Usage Instead of writing custom code for specif
Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
Filtration Curves for Graph Representation This repository provides the code from the KDD'21 paper Filtration Curves for Graph Representation. Depende
Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021
Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba
An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.
简介 通过PaddlePaddle框架复现了论文 Real-time Convolutional Neural Networks for Emotion and Gender Classification 中提出的两个模型,分别是SimpleCNN和MiniXception。利用 imdb_crop
The official implementation of the IEEE S&P`22 paper "SoK: How Robust is Deep Neural Network Image Classification Watermarking".
Watermark-Robustness-Toolbox - Official PyTorch Implementation This repository contains the official PyTorch implementation of the following paper to
Easy to use Audio Tagging in PyTorch
Audio Classification, Tagging & Sound Event Detection in PyTorch Progress: Fine-tune on audio classification Fine-tune on audio tagging Fine-tune on s
Guesslang detects the programming language of a given source code
Detect the programming language of a source code
Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
Framework overview This library allows to quickly implement different architectures based on Reservoir Computing (the family of approaches popularized
A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).
Torch-RGCN Torch-RGCN is a PyTorch implementation of the RGCN, originally proposed by Schlichtkrull et al. in Modeling Relational Data with Graph Conv
Parametric Contrastive Learning (ICCV2021)
Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or
This is an official implementation of the High-Resolution Transformer for Dense Prediction.
High-Resolution Transformer for Dense Prediction Introduction This is the official implementation of High-Resolution Transformer (HRT). We present a H
Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.
Graph Convolutional Networks for Hyperspectral Image Classification Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot T
Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification Code release for The Devil is in the Channels: Mutual-Channel
Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.
vanilla-rw-protonets-project Vanilla Prototypical Networks and PNs with Random Weights for image classification on Omniglot and mini-ImageNet. Made wi
Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
This is official implementaion of paper "Token Shift Transformer for Video Classification".
This is official implementaion of paper "Token Shift Transformer for Video Classification". We achieve SOTA performance 80.40% on Kinetics-400 val. Paper link
Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network
Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network
Official implementation of paper "Query2Label: A Simple Transformer Way to Multi-Label Classification".
Introdunction This is the official implementation of the paper "Query2Label: A Simple Transformer Way to Multi-Label Classification". Abstract This pa
Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)
Hierarchical Metadata-Aware Document Categorization under Weak Supervision This project provides a weakly supervised framework for hierarchical metada
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
FPGA & FreeNet Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification by Zhuo Zheng, Yanfei Zhong, Ailong M