jackieyung Goto Github PK

30min_guides

覃健祥的学习笔记，各种几十分钟入门的文档

adgan

The Implementation of paper "Controllable Person Image Synthesis with Attribute-Decomposed GAN"

auto-gpt

An experimental open-source attempt to make GPT-4 fully autonomous.

awesome-computer-vision

A curated list of awesome computer vision resources

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

caffe

Caffe: a fast open framework for deep learning.

caffe-1

Caffe fork with unpool layers, deconvolution layers, locally connected layers, and a custom layer called TweakFeaturesLayer.

caffe-windows

Configure Caffe in one hour for Windows users.

chainer-fast-neuralstyle

Chainer implementation of "Perceptual Losses for Real-Time Style Transfer and Super-Resolution".

chainer-fast-neuralstyle-models

Models for the chainer fast neuralstyle

convert_torch_to_pytorch

Convert torch t7 model to pytorch model and source.

deep-residual-networks

Deep Residual Learning for Image Recognition

deeplearningbook

MIT Deep Learning Book in PDF format

dig-into-apollo

Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.

example

tensorflow project example

fast-neural-style

Feedforward style transfer

fast-neural-style-1

pytorch implementation of fast-neural-style

fb-caffe-exts

Model Converter from Torch to Caffe

flops-counter.pytorch

Flops counter for convolutional networks in pytorch framework

gitignore

A collection of useful .gitignore templates

guided-filter

Fast and complete guided filter implementation for OpenCV

handong1587.github.io

higherhrnet-human-pose-estimation

This is an official implementation of our CVPR 2020 paper "HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation" (https://arxiv.org/abs/1908.10357)

igan

iGAN: Interactive Image Generation powered by GAN

imagefilter

图像处理常见滤镜，VS2013+OpenCV249

jackieyung.github.io

ktx-software

KTX (Khronos Texture) Library and Tools

learngit

neural-enhance

Super Resolution for images using deep learning.

neural-network-papers