dselivanov

text2vec

Fast vectorization, topic modeling, distances and GloVe word embeddings in R.

rsparse

Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.

robosat.pink

Computer Vision framework for GeoSpatial Imagery

BlitzML

shinycamera

Video stream input to shiny applications

AerialDetection

kmeansiou

k-means with IoU distance. Useful for anchor initialization in YOLO

RetinaNet_Tensorflow_Rotation

Focal Loss for Dense Rotation Object Detection

Rvision

Basic computer vision library for R

rweekly.org

R Weekly

torch

torch from R!

actions

GitHub Actions for the R community

albumentations

fast image augmentation library and easy to use wrapper around other libraries

armacmp

🚀 Automatically compile linear algebra R code to C++ with Armadillo

chainer

A flexible framework of neural networks for deep learning

Complex-YOLOv4-Pytorch

The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds"

ctv-model-deployment

CRAN Task View: Model Deployment with R

darknet

Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)

docker-alpine-R-REST

Docker image for REST server using Rserve proxy based on minimal alpine image

longears

A RabbitMQ client for R

nsqio.github.io

NSQ documentation

OnlineMiningTripletLoss

PyTorch conversion of https://omoindrot.github.io/triplet-loss

py-markdown-table

Tiny python library with zero dependencies which helps you to generate formatted markdown tables

pytorch-YOLOv4

Minimal PyTorch implementation of YOLOv4

rcpphnsw

Rcpp bindings for the approximate nearest neighbors library hnswlib

redux

:telephone_receiver::computer: Redis client for R

tensorrt-inference-server

The TensorRT Inference Server provides a cloud inferencing solution optimized for NVIDIA GPUs.

uv-cpp

libuv wrapper in C++11 /libuv C++11网络库

uvnsq

Async C++ client library for NSQ

yolov3

YOLOv3: Training and inference in PyTorch