soulnd's repositories
axios
Promise based HTTP client for the browser and node.js
OnJava8
《On Java 8》中文版，又名《Java编程**》 第5版
you-get
:arrow_double_down: Dumb downloader that scrapes the web
faceswap
Deepfakes Software For All
jadx
Dex to Java decompiler
cvpr2019
cvpr2019 papers，极市团队整理
glide
An image loading and caching library for Android focused on smooth scrolling
okhttp
An HTTP+HTTP/2 client for Android and Java applications.
awesome-python3-webapp
小白的Python入门教程实战篇：网站+iOS App源码→ http://t.cn/R2PDyWN 赞助→ http://t.cn/R5bhVpf
elasticsearch-analysis-ik
The IK Analysis plugin integrates Lucene IK analyzer into elasticsearch, support customized dictionary.
guava
Google core libraries for Java
LitePal
An Android library that makes developers use SQLite database extremely easy.
spark
Mirror of Apache Spark
Kibana_Hanization
Kibana 中文汉化
shadowsocks-windows
If you want to keep a secret, you must also hide it from yourself.
learngit-1
教程→ http://t.cn/zQ6LFwE 赞助→ http://t.cn/R5bhVpf 推送请使用UTF-8编码
booksource
《第一行代码 第2版》全书源代码
MNet_DeepCDR
Code for TMI 2018 "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation"
ML-Tutorial-Experiment
Coding the Machine Learning Tutorial for Learning to Learn
CapsNet-Tensorflow
A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules
pix2pix-tensorflow
Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets https://phillipi.github.io/pix2pix/
models
Models built with TensorFlow
hed
code for Holistically-Nested Edge Detection
fcn.berkeleyvision.org
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.
fundus-vessel-segmentation-tbme
In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.