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lxy6's Projects

acnet icon acnet

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks

albumentations icon albumentations

fast image augmentation library and easy to use wrapper around other libraries

computer-science icon computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

deep-high-resolution-net.pytorch icon 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_gcns icon deep_gcns

Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral

deeplearningpython35 icon deeplearningpython35

neuralnetworksanddeeplearning.com integrated scripts for Python 3.5.2 and Theano with CUDA support

hed icon hed

code for Holistically-Nested Edge Detection

kalman-and-bayesian-filters-in-python icon kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

mlbook icon mlbook

Repository for the free online book Machine Learning from Scratch (link below!)

pix2pixhd icon pix2pixhd

Synthesizing and manipulating 2048x1024 images with conditional GANs

pretrained-models.pytorch icon pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

pytorch-gan icon pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

pytorch-image-models icon pytorch-image-models

PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more

pytorch-triplet-loss icon pytorch-triplet-loss

re-implementation of triplet loss and triplet mining strategies (batch all and batch hard)

singan icon singan

Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

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