sndnshr

sndnshr's Stars

• jwasham/coding-interview-university

  A complete computer science study plan to become a software engineer.

  309k8.4k50777.4k

• huggingface/pytorch-image-models

  The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

  Language:Python32.8k3189474.8k

• lucidrains/vit-pytorch

  Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

  Language:Python21.3k1562693.1k

• vdumoulin/conv_arithmetic

  A technical report on convolution arithmetic in the context of deep learning

  Language:TeX14.1k343312.3k

• google-research/vision_transformer

  Language:Jupyter Notebook10.7k1062071.3k

• satellite-image-deep-learning/techniques

  Techniques for deep learning with satellite & aerial imagery

  8.9k279241.5k

• sfikas/medical-imaging-datasets

  A list of Medical imaging datasets.

  2.3k615421

• BangguWu/ECANet

  Code for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

  Language:Python1.3k1568199

• JunMa11/MICCAI-OpenSourcePapers

  MICCAI 2019-2023 Open Source Papers

  1.3k329237

• bupt-ai-cz/LLVIP

  LLVIP: A Visible-infrared Paired Dataset for Low-light Vision

  Language:Jupyter Notebook657253865

• andreasveit/densenet-pytorch

  A PyTorch Implementation for Densely Connected Convolutional Networks (DenseNets)

  Language:Python46649144

• icey-zhang/SuperYOLO

  SuperYOLO is accepted by TGRS

  Language:Python362214059

• DocF/multispectral-object-detection

  Multispectral Object Detection with Yolov5 and Transformer

  Language:Python34568164

• CalayZhou/Multispectral-Pedestrian-Detection-Resource

  A list of resouces for multispectral pedestrian detection,including the datasets, methods, annotations and tools.

  212101131

• Linfeng-Tang/SwinFusion

  This is official Pytorch implementation of "SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer"

  Language:Python20334320

• FrancescoSaverioZuppichini/ResNet

  Clean, scalable and easy to use ResNet implementation in Pytorch

  Language:Jupyter Notebook2007546

• px39n/Awesome-Data-Fusion-for-Remote-Sensing

  1936033

• Linfeng-Tang/MSRS

  MSRS: Multi-Spectral Road Scenarios for Practical Infrared and Visible Image Fusion

  Language:Python1492721

• hli1221/imagefusion-rfn-nest

  RFN-Nest(Information Fusion, 2021, Highly Cited Paper) - PyTorch =1.5，Python=3.7

  Language:Python11941819

• Vibashan/Image-Fusion-Transformer

  Official Pytorch Codebase for Image-Fusion-Transformer

  Language:Python1115914

• VasanthVanan/computer-networking-top-down-approach-notes

  Notes from "Computer Networking: A Top-down Approach"

  884022

• hli1221/imagefusion-nestfuse

  NestFuse (IEEE TIM 2020, Highly Cited Paper)- Pytorch >= 0.4.1

  Language:Python843823

• StaRainJ/road-scene-infrared-visible-images

  Datasets: road-scene-infrared-visible-images, for feature matching, image registration, and image fusion

  572538

• thfylsty/ImageFusion_Dualbranch_Fusion

  A Dual branch Network for Infrared and Visible Image Fusion (published by ICPR2020)

  Language:Python54136

• uzeful/Infrared-and-Visual-Image-Fusion-via-Infrared-Feature-Extraction-and-Visual-Information-Preservation

  Code for "Infrared and Visual Image Fusion through Infrared Feature Extraction and Visual Information Preservation"

  Language:Matlab485015

• UkcheolShin/Awesome-Thermal-Deep-3D-Vision

  35102

• kimphys/DIFNet.pytorch

  PyTorch implementation of Unsupervised Deep Image Fusion With Structure Tensor Representations

  Language:Python34123

• YogeshPhalak/Wavelet-Analysis-Image-Compression-Using-Discrete-Haar-Wavelet-Transform.

  In this project, we will present an example of an orthonormal system on [0,1) known as the Haar system. The Haar basis is the simplest and historically the first example of an orthonormal wavelet basis. Many of its properties stand in sharp contrast to the corresponding properties of the trigonometric basis (Fourier Basis). For example, (1) The Haar basis functions are supported on small subintervals of [0,1), whereas the Fourier basis functions are nonzero on all of [0,1), (2) The Haar basis functions are step functions with jump discontinuities, whereas the Fourier basis functions are C-infinity on [0,1), (3) The Haar basis replaces the notion of frequency (represented by the index n in the Fourier basis) with the dual notions of scale and location (separately indexed by j and k), (4) the Haar basis provides a very efficient representation of functions that consist of smooth, slowly varying segments punctuated by sharp peaks and discontinuities, whereas the Fourier basis best represents functions that exhibit long term oscillatory behavior.

  Language:TeX9002

• nish03/FMIVis

  Code for ISBI 2020 paper "Visualisation of Medical Image Fusion and Translation for Accurate Diagnosis of High Grade Gliomas"

  Language:MATLAB5101

• YeyaoChen/MPME_LF_Dataset

  2100