Simon Donike (simon-donike)

simon-donike

Geek Repo

Company:University of Valencia, Spain

Location:Valencia, Spain

Home Page:donike.net

Twitter:@simon_donike

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Organizations
eo4geocourses
ESAOpenSR
PLUS-Seasy

Simon Donike's starred repositories

private-gpt

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

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latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

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techniques

Techniques for deep learning with satellite & aerial imagery

PerceptualSimilarity

LPIPS metric. pip install lpips

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Image-Super-Resolution-via-Iterative-Refinement

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

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keras-io

Keras documentation, hosted live at keras.io

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EDSR-PyTorch

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

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a-PyTorch-Tutorial-to-Super-Resolution

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution

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DiffusionFastForward

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

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RDN

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

SRCNN-pytorch

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

albumentations_examples

Augmentations usage examples for albumentations library

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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

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worldstrat

The WorldStrat Dataset

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yoltv5

YOLT, now with PyTorch.

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sr4rs

Super resolution for remote sensing

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super-image

Image super resolution models for PyTorch.

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iris

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

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pytorch-super-resolution-model-collection

Collection of Super-Resolution models via PyTorch

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.

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highresnet

PyTorch implementation of HighRes3DNet

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Generative-Models

Repository of Various Test & Implementation of Generative Models

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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.

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opensr-test

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

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Morans_I

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

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CodeMonkeys

CodeMonkeys project for Dracco Hackaton at Manresa

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remicres.github.io

Personal web site

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