ChiaYi-LIN's repositories
zku-cohort-week2
week 2 coding assignment
mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
STDC-Seg
Source Code of our CVPR2021 paper "Rethinking BiSeNet For Real-time Semantic Segmentation"
mmdeploy
OpenMMLab Model Deployment Framework
PaddleSeg
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
X2Paddle
Deep learning model converter for PaddlePaddle. (『飞桨』深度ĺ¦äą 模型转换工具)
BasicSR
Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.
semantic-segmentation-pytorch
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR
Mask3D
Mask3D predicts accurate 3D semantic instances achieving state-of-the-art on ScanNet, ScanNet200, S3DIS and STPLS3D.
SwinIR
SwinIR: Image Restoration Using Swin Transformer (official repository)
LanguageGroundedSemseg
Implementation for ECCV 2022 paper Language-Grounded Indoor 3D Semantic Segmentation in the Wild
cityscapesScripts
README and scripts for the Cityscapes Dataset
DenseCLIP
[CVPR 2022] DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
Uformer
[CVPR 2022] Official repository for the paper "Uformer: A General U-Shaped Transformer for Image Restoration".
USISResNet
This repository includes code of training/testing of our work published in NTIRE-2020 workshop titled "Unsupervised Single Image Super-Resolution Network (USISResNet) for Real-World Data Using Generative Adversarial Network".
DUSGAN
Official code of "Direct Unsupervised Super-Resolution using Generative Adversarial Network (DUS-GAN) for Real-World Data" published in an IEEE Transaction in Image Processing (TIP).
EdgeNets
This repository contains the source code of our work on designing efficient CNNs for computer vision