linhduongtuan Goto Github PK

followers: 102.0 following: 124.0 repos: 1.2K gists: 5.0

Name: Linh T. Duong

Type: User

Company: KTH Royal Institute of Technology

Bio: Obtain a PhD in medical microbiology from the University of Greifswald, Germany. Interested in Microbiology, Molecular biology, Epidemiology, & Machine Learning

Twitter: DuongTuanLinh1

Location: Stockholm - Sweden

Blog: https://scholar.google.com/citations?user=aZKRy1oAAAAJ&hl=en

Linh T. Duong's Projects

object-cxr

Automatic detection of foreign objects on chest X-rays

object-detection-and-counting-faster-r-cnn

A vision-based tool for transport system flow analysis using Faster R-CNN. This system is able to detect different types of objects, e.g., cars, buses, pedestrians, and classify as well as count them in transport videos with a real-time speech.

object-detection-and-image-segmentation-with-detectron2

Object Detection and Image Segmentation with Detectron2

object-detection-metrics

Most popular metrics used to evaluate object detection algorithms.

objectdetection

Some experiments with object detection in PyTorch

ocaml-torch

OCaml bindings for PyTorch

ocrmypdf-flask-example

A simple implementation of ocrmypdf and tesseract with flask for hosting to a server as an API. The code was written on CentOS7. This code works on linux only as ocrmypdf library does not have support on windows because of missing leptonica dll. For windows consider https://github.com/lakshay1296/OCR_Conversion_JPEG2PDF. This is image to ocr pdf conversion.

ocropy

Python-based tools for document analysis and OCR

ohem

OHEM support for Fast R-CNN

oidv4_toolkit

Download and visualize single or multiple classes from the huge Open Images v4 dataset

onnx-heartbeat

ONNX.js tutorial for heartbeat by Fritz.ai

opencv

Open Source Computer Vision Library

openff-benchmark

Comparison benchmarks between public force fields and Open Force Field Initiative force fields

openhine

An Open-Source Toolkit for Heterogeneous Information Network Embedding (HINE)

openmm-ml

High level API for using machine learning models in OpenMM simulations

opennmt-py

Open Source Neural Machine Translation in PyTorch

optic-disc-unet

Attention Unet model with post process for retina optic disc segmention

paddle

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

paddledetection

Object detection and instance segmentation toolkit based on PaddlePaddle.

paddleocr

Awesome OCR toolkits based on PaddlePaddle （3.5M practical ultra lightweight OCR system, support training and deployment among server, mobile, embedded and IoT devices）

panini_tutorial

Deploy Pytorch models to production via panini

pas

The released code for the paper: Pooling Architecture Search for Graph Classification, in CIKM 2021.

pelee

Pelee: A Real-Time Object Detection System on Mobile Devices

penguin-inference-api

API code for fastai Penguin Inference (deployed as AWS Lambda function)

perception-for-self-driving

Perception algorithms for Self-driving car; Lane Line Finding, Vehicle Detection, Traffic Sign Classification algorithm.

pgap

NCBI Prokaryotic Genome Annotation Pipeline

phobert-sentiment-classification

Sentiment classification for Vietnamese text using PhoBert

plant_disease_detection

Plant Disease Detector Web Application

pna

Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric

pneumonia-diagnosis-using-xrays-96-percent-recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

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