linhduongtuan Goto Github PK

oidv4_toolkit

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

olm-training

Repo for training MLMs, CLMs, or T5-type models on the OLM pretraining data, but it should work with any hugging face text 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.

pneumothorax-segmentation

https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation

pneumothorax-segmentation-1

SIIM-ACR Pneumothorax Segmentation first place solution

pneumothorax-segmentation-2nd-place-solution

pointnet2_randaugment

Applying RandAugment on PointNet++

polars_vs_pandas

I try to compare performance of Polars and Pandas in reading large files using *csv, *parquet and *feather formats

post-tuto-deployment

Build and deploy a machine learning app from scratch 🚀

ppnp

PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)

practical-pytorch

PyTorch tutorials demonstrating modern techniques with readable code