simon-donike

simon-donike's Stars

• zylon-ai/private-gpt

  Interact with your documents using the power of GPT, 100% privately, no data leaks

  Language:Python54.7k4601.2k7.4k

• CompVis/latent-diffusion

  High-Resolution Image Synthesis with Latent Diffusion Models

  Language:Jupyter Notebook12.2k983481.6k

• milesial/Pytorch-UNet

  PyTorch implementation of the U-Net for image semantic segmentation with high quality images

  Language:Python9.5k714492.5k

• satellite-image-deep-learning/techniques

  Techniques for deep learning with satellite & aerial imagery

  8.9k279241.5k

• Janspiry/Image-Super-Resolution-via-Iterative-Refinement

  Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch

  Language:Python3.7k65139473

• f4exb/sdrangel

  SDR Rx/Tx software for Airspy, Airspy HF+, BladeRF, HackRF, LimeSDR, PlutoSDR, RTL-SDR, SDRplay and FunCube

  Language:C++3k1451.7k453

• keras-team/keras-io

  Keras documentation, hosted live at keras.io

  Language:Jupyter Notebook2.8k616302.1k

• sanghyun-son/EDSR-PyTorch

  PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

  Language:Python2.5k40345675

• mikonvergence/DiffusionFastForward

  DiffusionFastForward: a free course and experimental framework for diffusion-based generative models

  Language:Jupyter Notebook61510857

• yjn870/SRCNN-pytorch

  PyTorch implementation of Image Super-Resolution Using Deep Convolutional Networks (ECCV 2014)

  Language:Python559722124

• yulunzhang/RDN

  Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

  Language:Lua5541627109

• albumentations-team/albumentations_examples

  Augmentations usage examples for albumentations library

  Language:Python42791699

• debidatta/syndata-generation

  Code used to generate synthetic scenes and bounding box annotations for object detection. This was used to generate data used in the Cut, Paste and Learn paper

  Language:Python29071872

• worldstrat/worldstrat

  The WorldStrat Dataset

  Language:Jupyter Notebook24061526

• allenai/satlas-super-resolution

  Language:Python23564629

• avanetten/yoltv5

  YOLT, now with PyTorch.

  Language:Python19671229

• remicres/sr4rs

  Super resolution for remote sensing

  Language:Python17346619

• eugenesiow/super-image

  Image super resolution models for PyTorch.

  Language:Python1642720

• ESA-PhiLab/iris

  Semi-automatic tool for manual segmentation of multi-spectral and geo-spatial imagery.

  Language:JavaScript141113042

• togheppi/pytorch-super-resolution-model-collection

  Collection of Super-Resolution models via PyTorch

  Language:Python916237

• aietal/aimengpt

  A self-hosted, offline, ChatGPT-like chatbot that allows document uploads, powered by Llama 2, chromadb and Langchain. 100% private, with no data leaving your device. New: Support for Code Llama models.

  Language:TypeScript77226

• fepegar/highresnet

  PyTorch implementation of HighRes3DNet

  Language:Python4931615

• KiUngSong/Generative-Models

  Repository of Various Test & Implementation of Generative Models

  Language:Jupyter Notebook41208

• debasis-dotcom/Ship-Detection-from-Satellite-Images-using-YOLOV4

  Ship detection from remote sensing imagery is a crucial application for maritime security which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. This is typically done through the use of an Automated Identification System (AIS), which uses VHF radio frequencies to wirelessly broadcast the ships location, destination and identity to nearby receiver devices on other ships and land-based systems. AIS are very effective at monitoring ships which are legally required to install a VHF transponder, but fail to detect those which are not, and those which disconnect their transponder. So how do you detect these uncooperative ships? This is where satellite imagery can help. Synthetic Aperture Radar (SAR) imagery uses radio waves to image the Earth’s surface. Unlike optical imagery, the wavelengths which the instruments use are not affected by the time of day or meteorological conditions, enabling imagery to be obtained day or night, with cloudy, or clear skies. Satellites are collecting these images which could be used to make algorithms for ship detection and segmentation.

  Language:Jupyter Notebook375111

• ESAOpenSR/opensr-test

  A comprehensive benchmark for real-world Sentinel-2 imagery super-resolution

  Language:Python20330

• simon-donike/Morans_I

  Using Python to calculate Moran's I from a TIFF-Image

  Language:Jupyter Notebook6101

• hafidk/CodeMonkeys

  CodeMonkeys project for Dracco Hackaton at Manresa

  Language:Jupyter Notebook3200

• ESAOpenSR/opensr-utils

  Language:Python2240

• remicres/remicres.github.io

  Personal web site

  Language:JavaScript2101

• annaporti/COVID-19_Dashboards

  Language:Python110