SanKumSan

SanKumSan's Stars

• tudarmstadt-lt/GermaNER

  GermaNER: Free Open German Named Entity Recognition Tool

  Language:Java3613

• anisha2102/docvqa

  Document Visual Question Answering

  Language:Python11525

• jalammar/jalammar.github.io

  Build a Jekyll blog in minutes, without touching the command line.

  Language:Jupyter Notebook835383

• SamLynnEvans/Transformer

  Transformer seq2seq model, program that can build a language translator from parallel corpus

  Language:Python1.4k350

• GokuMohandas/Made-With-ML

  Learn how to design, develop, deploy and iterate on production-grade ML applications.

  Language:Jupyter Notebook38.4k6.1k

• abhishekkrthakur/bert-entity-extraction

  Language:Python12459

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python142k28.5k

• microsoft/unilm

  Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

  Language:Python21k2.6k

• doc-analysis/DocBank

  DocBank: A Benchmark Dataset for Document Layout Analysis

  Language:Python60374

• google-research/bert

  TensorFlow code and pre-trained models for BERT

  Language:Python39k9.7k

• AndriyMulyar/bert_document_classification

  architectures and pre-trained models for long document classification.

  Language:Python15447

• NielsRogge/Transformers-Tutorials

  This repository contains demos I made with the Transformers library by HuggingFace.

  Language:Jupyter Notebook10.3k1.5k

• microsoft/opensource.microsoft.com

  This is the source code to the Microsoft Open Source site featuring projects, program information, and "get involved" pages. This site is published at opensource.microsoft.com and managed by the Microsoft Open Source Programs Office (OSPO).

  Language:JavaScript884413

• suvoooo/Learn-TensorFlow

  Learning Tensorflow Step by Step:: Concepts, Examples & Applications

  Language:Jupyter Notebook5849

• marcopeix/Deep_Learning_AI

  Language:Jupyter Notebook131157

• krishnaik06/Advanced-CNN-Architectures

  Language:Jupyter Notebook4757

• kuangliu/torchcv

  TorchCV: a PyTorch vision library mimics ChainerCV

  Language:Python39570

• groverpr/deep-learning

  Language:Jupyter Notebook1817

• deepakrox/FastObjectLocalization

  Language:Jupyter Notebook247

• pavitrakumar78/Street-View-House-Numbers-SVHN-Detection-and-Classification-using-CNN

  A 2-CNN pipeline to do both detection (using bounding box regression) and classification of numbers on SVHN dataset.

  Language:Python6023

• nalepae/bounding-box

  Bounding Box is a library to plot pretty bounding boxes with a simple Python API.

  Language:Python9919

• dhlab-epfl/dhSegment

  Generic framework for historical document processing

  Language:Python375115

• tblock/10kGNAD

  Ten Thousand German News Articles Dataset for Topic Classification

  Language:Python8415

• ultralytics/yolov3

  YOLOv3 in PyTorch > ONNX > CoreML > TFLite

  Language:Python10.4k3.5k

• ShairozS/Scan2Topic

  A system for reading scanned documents and grouping them into high level topics

  Language:Python167

• qq456cvb/multi-stage-detection

  Tensorflow implementation of "MULTI-STAGE REINFORCEMENT LEARNING FOR OBJECT DETECTION"

  Language:Python152

• gitanat/simple-ocr-opencv

  A simple python OCR engine using opencv

  Language:Python529178

• jasmcaus/opencv-course

  Learn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.

  Language:Python1.2k1k

• javedsha/text-classification

  Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK

  Language:Jupyter Notebook290206

• sambalshikhar/Document-Image-Classification-with-Intra-Domain-Transfer-Learning-and-Stacked-Generalization-of-Deep

  RVL-CDIP could be looked at as the equivalent of ImageNet for the document image community. It’s certainly the largest we’ve seen in the literature. There are 400,000 total document images in the dataset. The dataset contains much noise and variance in composition of each document class. Uncompressed, the dataset size is ~100GB, and comprises 16 classes of document types, with 25,000 samples per classes. Example classes include email, resume, and invoice. Achieved an Accuracy of over 93% which beat the benchmark score of 92% based on https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip

  Language:Jupyter Notebook188