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KimHyunJune's Projects

golfdb icon golfdb

GolfDB: A Video Database for Golf Swing Sequencing

grpc icon grpc

The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)

headpose-estimator-apache-mxnet icon headpose-estimator-apache-mxnet

Head Pose estimator using Apache MXNet. HeadPose_ResNet50_Tutorial.ipynb helps you to walk through an entire work flow of developing a CNN model from the scratch including data augmentation, fine-tuning, training, saving check-point model artifacts, validation and inference.

hjkim icon hjkim

전체적으로 수정!, 부족한 내용은 차후 추가하는 방향으로

imgaug icon imgaug

Image augmentation for machine learning experiments.

kvm-docker-comparison icon kvm-docker-comparison

Experiments for the ISPASS 2015 poster "An Updated Performance Comparison of Virtual Machines and Linux Containers"

macvim icon macvim

Vim - the text editor - for macOS

mask_rcnn icon mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

maskrcnn-benchmark icon maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

mne-python icon mne-python

MNE : Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

models icon models

Models and examples built with TensorFlow

myscript-text-web icon myscript-text-web

:pencil2: :cloud: The easy way to integrate text handwriting recognition in your web app.

parallelize-ml-inference icon parallelize-ml-inference

Sample code for parallelizing across multiple CPU/GPUs on a single machine to speed up deep learning inference

protobuf icon protobuf

Protocol Buffers - Google's data interchange format

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytriton icon pytriton

PyTriton is a Flask/FastAPI-like interface that simplifies Triton's deployment in Python environments.

rapid icon rapid

An extension of the iD map editor for mapping with AI-generated features.

sagemaker-containers icon sagemaker-containers

This support code is used for making machine learning frameworks (e.g. MXNet, TensorFlow) run on Amazon SageMaker.

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