Zhiqiang Lang (pppLang)

pppLang

Geek Repo

Company:Northwestern Polytechnical University

Location:Xi'an Shaanxi, 710072, P.R.China.

Home Page:https://ppplang.github.io/

Github PK Tool:Github PK Tool

Zhiqiang Lang's repositories

SKNet

try to implement the CVPR 2019 paper "Selective Kernel Networks" by PyTorch

Res2Net

Try to implement and test CVPR 2019 paper "Res2Net: A New Multi-scale Backbone Architecture" in PyTorch.

FMNetforSSR

The implementation code of AAAI 2020 paper "Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution".

SISRBNN

Try to implement "Efficient Super Resolution Using Binarized Neural Network" by PyTorch.

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E2FIF

Simple but stronge baseline for binarized super-resolution networks (BSRNs).

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AIM2019

The testing code and pretrained model for AIM Workshop and Challenge @ ICCV 2019.

NetworkQuantification

try to implement network quantification, from xnor-net and bi-real net

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awesome-NeRF

A curated list of awesome neural radiance fields papers

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ICIP2019_Deraing

ICIP2019 Deraing changlle

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Non-Local-NN-Pytorch

Pyorch implementation of Non-Local Neural Networks (https://arxiv.org/pdf/1711.07971.pdf)

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pppLang.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

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pytorch-cifar

95.16% on CIFAR10 with PyTorch

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Pytorch-UNet

Pytorch implementation of the U-Net for image semantic segmentation, with dense CRF post-processing

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pytorch_resnet_cifar10

Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.

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resnet-cifar10-caffe

ResNet-20/32/44/56/110 on CIFAR-10 with Caffe

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ResNet-on-Cifar10

Reimplementation ResNet on cifar10 with caffe

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VAE

implement a VAE demo

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XNOR-Net-PyTorch

PyTorch Implementation of XNOR-Net

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yolov7

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

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YOLOX

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

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