wk910930

wk910930's Stars

• tldr-pages/tldr

  📚 Collaborative cheatsheets for console commands

  Language:Markdown50.7k3711.3k4.2k

• ziishaned/learn-regex

  Learn regex the easy way

  45.6k900866.2k

• isocpp/CppCoreGuidelines

  The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++

  Language:CSS42.6k2k1.2k5.4k

• koalaman/shellcheck

  ShellCheck, a static analysis tool for shell scripts

  Language:Haskell36.2k4162.7k1.8k

• yunjey/pytorch-tutorial

  PyTorch Tutorial for Deep Learning Researchers

  Language:Python29.9k6241798.1k

• facebookresearch/Detectron

  FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

  Language:Python26.2k9409265.5k

• HarisIqbal88/PlotNeuralNet

  Latex code for making neural networks diagrams

  Language:TeX21.9k2271222.9k

• antonmedv/fx

  Terminal JSON viewer & processor

  Language:Go19k118206441

• onnx/onnx

  Open standard for machine learning interoperability

  Language:Python17.7k4392.8k3.7k

• Bash-it/bash-it

  A community Bash framework.

  Language:Shell14.2k3256352.3k

• lanpa/tensorboardX

  tensorboard for pytorch (and chainer, mxnet, numpy, ...)

  Language:Python7.9k84450865

• afshinea/stanford-cs-230-deep-learning

  VIP cheatsheets for Stanford's CS 230 Deep Learning

  6.3k22671.4k

• luanfujun/deep-painterly-harmonization

  Code and data for paper "Deep Painterly Harmonization": https://arxiv.org/abs/1804.03189

  Language:Cuda6.1k17246627

• rafaelpadilla/Object-Detection-Metrics

  Most popular metrics used to evaluate object detection algorithms.

  Language:Python4.9k701501k

• bethgelab/foolbox

  A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

  Language:Python2.7k46371425

• open-mpi/ompi

  Open MPI main development repository

  Language:C2.1k1203.6k858

• mit-han-lab/proxylessnas

  [ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

  Language:C++1.4k700284

• kemaloksuz/ObjectDetectionImbalance

  Lists the papers related to imbalance problems in object detection [TPAMI]

  1.1k714175

• Robert-JunWang/Pelee

  Pelee: A Real-Time Object Detection System on Mobile Devices

  Language:Jupyter Notebook8855184255

• rgeirhos/texture-vs-shape

  Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral)

  Language:R7841930101

• jonbarron/robust_loss_pytorch

  A pytorch port of google-research/google-research/robust_loss/

  Language:Python659133288

• ronghanghu/seg_every_thing

  Code release for Hu et al., Learning to Segment Every Thing. in CVPR, 2018.

  Language:Python425231472

• imatge-upc/detection-2016-nipsws

  Hierarchical Object Detection with Deep Reinforcement Learning

  Language:Python4242636129

• danielewworrall/harmonicConvolutions

  Deep Translation and Rotation Equivariance

  Language:Python267121448

• unsky/Deformable-ConvNets-caffe

  Deformable Convolutional Networks on caffe

  Language:Jupyter Notebook160103683

• afantideng/R-FCN-PSROIAlign

  A Caffe implementation of PSROI-Align

  Language:Jupyter Notebook552420

• RuiminChen/Caffe-MobileNetV2-ReLU6

  Caffe implementation of ReLU6 Layer

  Language:C++465217

• alex-lew/robot-mind-meld

  A little game powered by word vectors

  Language:JavaScript31413

• TianzhongSong/caffe_kld_loss

  caffe KL Divergence Loss layer for matching prob distribution

  Language:C++3101

• sunyuhan19981208/Multi-LoRA-LLM

  Multi-LoRA-LLM is a project that aims to enhance the performance of LLM (Language Model with Latent Retrieval and Alignment) by utilizing multiple LoRA (Latent Retrieval and Alignment) models. This README provides instructions on how to use and evaluate the Multi-LoRA-LLM project.

  Language:Python2101