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qingsong99's Projects

optimal-lsh icon optimal-lsh

This package provides an efficient implementation of locality-sensitve hashing (LSH)

pbrtbook icon pbrtbook

pbrt 中文整合翻译 基于物理的渲染:从理论到实现 Physically Based Rendering: From Theory To Implementation

pifuhd icon pifuhd

High-Resolution 3D Human Digitization from A Single Image.

pix2pix icon pix2pix

Image-to-image translation using conditional adversarial nets

pix2pix-1 icon pix2pix-1

Real-time pix2pix implementation with Unity

pixel-art icon pixel-art

We implement the famous "Depixelizing Pixel Art" paper by Kopf and Lischinski

pose-hg-train icon pose-hg-train

Training and experimentation code used for "Stacked Hourglass Networks for Human Pose Estimation"

py-leveldb icon py-leveldb

Automatically exported from code.google.com/p/py-leveldb

pylearn2 icon pylearn2

A Machine Learning library based on Theano

pytorch-adain icon pytorch-adain

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]

pytorch-cifar100 icon pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet)

pytorch-image-models icon pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

pytorch-image-retrieval icon pytorch-image-retrieval

A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (CVPR 2017).

qix icon qix

Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang

qlib icon qlib

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies.

r2plus1d icon r2plus1d

R(2+1)D and Mixed-Convolutions for Action Recognition.

rcnn icon rcnn

R-CNN: Regions with Convolutional Neural Network Features

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