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ifc-to-gbxml-converter

Converter from Ifc to gbXML

image-segmentation-keras

Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

interview

📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。

keras

Deep Learning for humans

keras-fcn

Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation（Unfinished）

keras-fcn-1

A playable implementation of Fully Convolutional Networks with Keras.

latex

latex入门教程

ldh

ldsdownloader

https://github.com/lushuaifu/LDSDownloader

learningpython31days

自学python记录

libtorch_cpp_mobilenetv2_5classes

I use mobilenetv2 to export a model with 5 classes, then use libtorch to load and run it.

loffer

博客主题 A forkable Jekyll theme with Chinese UI and document

lushuaifu.github.io

machinecode

https://github.com/lushuaifu/MachineCode

matjek

Material theme based on Materialize.css for jekyll sites

mmcv

OpenMMLab Computer Vision Foundation

model-compression

model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution structure；4、batch-normalization folding for quantization)

models

Models and examples built with TensorFlow

models-1

Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型）

mypro

mytest

netron

Visualizer for neural network, deep learning and machine learning models

neuralnlp-neuralclassifier

An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

opencv_extra

OpenCV extra data

paddleseg

A high performance semantic segmentation toolkit based on PaddlePaddle. (『飞桨』图像分割库）

picreader

https://github.com/lushuaifu/picReader

plotneuralnet

Latex code for making neural networks diagrams

pspnet-keras-tensorflow

TensorFlow implementation of original paper : https://github.com/hszhao/PSPNet

pua-books

:books: PUA 书籍

python3-concurrency

Python3爬虫系列的理论验证，首先研究I/O模型，分别用Python实现了blocking I/O、nonblocking I/O、I/O multiplexing各模型下的TCP服务端和客户端。然后，研究同步I/O操作（依序下载、多进程并发、多线程并发）和异步I/O（asyncio）之间的效率差别