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afnetworking

A delightful networking framework for iOS, OS X, watchOS, and tvOS.

awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations.

awesome-hand-pose-estimation

Awesome work on hand pose estimation/tracking

bash-shell

OWenT's Utils -- Bash&Shell branch

basicocr

Basic Optical Character Recognition Tutorial. Damiles Blog.

bbox-label-tool

A simple tool for labeling object bounding boxes in images

bgslibrary

A background subtraction library

black-in-white

实现印象笔记的图片去底色白板功能

book

:books: All programming languages books

book-identifier

A Matlab app for identifying books from bookshelf images.

books

技术资料分享

caffewebapi

centernet

Object detection, 3D detection, and pose estimation using center point detection:

centernet-1

Codes for our paper "CenterNet: Keypoint Triplets for Object Detection" .

chainer-fast-rcnn

Chainer folk of Fast R-CNN (Object Detection Method)

chalearn_liveness_challenge

ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2019

cnn_sentence

CNNs for sentence classification

code

Code for the book "Mastering OpenCV with Practical Computer Vision Projects" by Packt Publishing 2012.

codereader

跟大咖一起读源码

cornernet

cppmt

crnn-train-pytorch

crnn trian pytorch

cv

My CV built using RMarkdown and the pagedown package.

cvpr19-face-anti-spoofing

Code for 2nd Place Solution in Face Anti-spoofing Attack Detection Challenge @ CVPR2019

cvpr2019

cvpr2019／cvpr2018/cvpr2019 papers，极市团队整理

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

deep-high-resolution-net.pytorch

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-reinforcement-learning-hands-on

Hands-on Deep Reinforcement Learning, published by Packt

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06