HandsomeJIm's repositories
addnoise
script and ubuntu executable to add noise to the given wavs
attention-module
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
Cpp-Primer
C++ Primer 5ed answers
DrumGAN
Synthesis of Drum Sounds With Perceptual Timbral Conditioning Using Generative Adversarial Networks
DuiLib_Redrain
A modified by Redrain's version of the Duilib
echarts3-chinese-map-drill-down
Echarts3**地图下钻至县级 🌏
environmental_sound_classification_1DCNN
Classification of environmental sounds using 1D convolutional Neural network
ESC-50
ESC-50: Dataset for Environmental Sound Classification
ganhacks
starter from "How to Train a GAN?" at NIPS2016
helloflask
Hello, Flask!
learnGitBranching
An interactive git visualization and tutorial. Aspiring students of git can use this app to educate and challenge themselves towards mastery of git!
lgtfb-en
Learnable Gammatone Filterbank (LGTFB) and Equal-loudness Normalization (EN)
libfacedetection
An open source library for face detection in images. The face detection speed can reach 1500FPS.
LibGb28181PsMux
PsMux for China GB28181
makefile_tutorial_project
https://zhuanlan.zhihu.com/p/396448133
melgan-neurips
GAN-based Mel-Spectrogram Inversion Network for Text-to-Speech Synthesis
MTCNN-light
this repository is the implementation of MTCNN with no framework, Just need opencv and openblas, support linux and windows
pytorch-cifar100
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
redis-plus-plus
Redis client written in C++
SinusoidalGAN
Sinusoidal function generating network based on adversarial learning
SupContrast
PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)
tinyrpc
c++ async rpc. 5w+qps.
ucore
清华大学操作系统课程实验 (OS Kernel Labs)
wgan-gp
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"