Pinned Repositories
anomalib
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
bgslibrary
A C++ Background Subtraction Library with wrappers for Python, MATLAB, Java and GUI on QT
DRepT
[IJCNN2023] Defect Representation Transfer-based Anomaly Detection (DRepT) in PyTorch
gmm_cpp
Gaussian Mixture Model Implementation from scratch in C++
lightgrad
Lightest Gradient Calculation Framework based on Define-by-Run for Deep Learning in C++
normal_distribution_dataset
1-dimensional shape dataset generated by random numbers of normal distribution
prml_julia
PRML sample programs in Julia
pytorch-optimizer
torch-optimizer -- collection of optimizers for Pytorch
pytorch_cpp
Deep Learning sample programs using PyTorch in C++
svm_cpp
Support Vector Machines Implementation from scratch in C++
koba-jon's Repositories
koba-jon/pytorch_cpp
Deep Learning sample programs using PyTorch in C++
koba-jon/svm_cpp
Support Vector Machines Implementation from scratch in C++
koba-jon/gmm_cpp
Gaussian Mixture Model Implementation from scratch in C++
koba-jon/DRepT
[IJCNN2023] Defect Representation Transfer-based Anomaly Detection (DRepT) in PyTorch
koba-jon/anomalib
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
koba-jon/bgslibrary
A C++ Background Subtraction Library with wrappers for Python, MATLAB, Java and GUI on QT
koba-jon/lightgrad
Lightest Gradient Calculation Framework based on Define-by-Run for Deep Learning in C++
koba-jon/normal_distribution_dataset
1-dimensional shape dataset generated by random numbers of normal distribution
koba-jon/prml_julia
PRML sample programs in Julia
koba-jon/pytorch-optimizer
torch-optimizer -- collection of optimizers for Pytorch
koba-jon/YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/