Ivan Derianto's repositories
LabelMeYoloConverter
Convert LabelMe Annotation Tool JSON format to YOLO text file format
TensorRT-Image-Classification
Windows - C++ Visual Studio solution for Image Classification using Caffe Model and TensorRT inference platform
YoloBBoxChecker
Program to extract value from YOLO format text file and draw a bounding box to the images
PerspectiveTransformer
Python GUI program to do Perspective Transformation using Mouse Click to map the 4 points coordinates.
Yolo_MultiClass_LabelTool
My own version to annotate dataset for YOLO format (Including multi-class labeling on the same image)
University-Project
Collection of programming project during my studies at university. Full version, download from this google drive link: https://drive.google.com/open?id=1OGZSbOsQTCgpixEvV5fbJzrTw6SRRu1T
audiograph
Windows AudioGraph native C++ with cppwinrt headers.
ConcurrentQueue
thread-safe queue sample program
docpagetest.github.io
The most advanced Video Player for Unity and Unreal Engine apps. It enables DRM-protected premium HLS and DASH video streaming inside games, metaverses as well as web3 projects based on blockchain on mobile, PC, laptop, web, and VR/AR headset.
EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
ivder.github.io
Personal profile page https://ivder.github.io/
learnopencv
Learn OpenCV : C++ and Python Examples
MobileNet-YOLO
A caffe implementation of MobileNet-YOLO detection network
NexPlayer_Unity_Plugin
Stream videos in HLS & DASH with Widevine DRM using NexPlayer Video Streaming Player SDK for Unity on Android & iOS devices
pytorch-caffe-darknet-convert
convert between pytorch, caffe prototxt/weights and darknet cfg/weights
pytorch-image-models
PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more
pytorch_GAN_zoo
A mix of GAN implementations including progressive growing
ResNet-50-101-152
This is an implementation of ResNet-50/101/152.
tensorflow
Computation using data flow graphs for scalable machine learning