zhangcats Goto Github PK

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

android-gif-drawable

Views and Drawable for displaying animated GIFs on Android

aodnet-by-pytorch

Image Dehaze, Pytorch, An All-in-One Network for Dehazing, AOD-Net

audio-reactive-led-strip

:musical_note: :rainbow: Real-time LED strip music visualization using Python and the ESP8266 or Raspberry Pi

awesome-wechat-weapp

微信小程序开发资源汇总 :100:

carnd-vehicle-detection

Vehicle Detection Project

coursera-ml-using-matlab-python

coursera吴恩达机器学习课程作业自写Python版本+MATLAB原版

darknet

Convolutional Neural Networks

douban

faceswap

Deepfakes Software For All

fallingcode

digital rain

fastmaskrcnn

Mask RCNN in TensorFlow

full-speed-python

hello-world

just another repository

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mediapipe

MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines

ml-in-action-code-and-note

:chart_with_upwards_trend:Machine Learning code in Python3.x. （机器学习实战 py3代码整理）Some notes about the practices:（for reference only）

mobilenet-ssd

Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

mobilenetv2

musicled

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

notes-python

中文 Python 笔记

opencv-practical-exercise

OpenCV practical exercise

pytorch-book

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

pytorch-yolov3

Minimal PyTorch implementation of YOLOv3

renren-fast-vue

renren-fast-vue基于vue、element-ui构建开发，实现renren-fast后台管理前端功能，提供一套更优的前端解决方案。

retina-unet

Retina blood vessel segmentation with a convolutional neural network

robotics-course-project

Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos

scrapy

scriptcrawler