Image marine sea litter prediction Shiny

Related tags

Deep LearningMARLIT
Overview

MARLITE

Shiny app for floating marine litter detection in aerial images.

This directory contains the instructions and software needed to install the Shiny MARLIT application on a local computer (Windows Operating System).

Detailed information on the application functioning and use is illustrated in: Automatic detection and quantification of floating marine macro-litter in aerial images: introducing a novel deep learning approach connected to a web application in R By: Odei Garcia-Garin, Toni Monleón-Getino, Pere López-Brosa, Asunción Borrell, Alex Aguilar, Ricardo Borja-Robalino, Luis Cardona, Morgana Vighi

The study describes the development of a CNN-based algorithm for the detection of floating marine macro litter in aerial images and its connection to a web-oriented application based on the Shiny package.

To use the application, it is necessary to have R (version 3.6) and Rstudio installed.

Follow the installation instructions described in the instal_paquets.R file and execute them in R.

Run the shiny application contained in the AllPlast directory from Rstudio, as it is usually done with Shiny applications (see https://rstudio.com/resources/webinars/introduction-to-shiny/)

Inside the test IMAGES folder you will find different images with and without floating litter that can be used to carry out tests. Otherwise, you can test the application by using your own aerial images, provided they respond to the requirements described in the study (images should be taken in the nadir position and the ground sampling distance should be at least 3.6 cm).

Please, contact us at [email protected] or [email protected] whether you have any doubts/comments regarding the functioning of the application, or in case you have any suggestions for its improvement or you are interested in sharing any set of aerial images of the sea surface.

For medical image segmentation

LeViT_UNet For medical image segmentation Our model is based on LeViT (https://github.com/facebookresearch/LeViT). You'd better gitclone its codes. Th

13 Dec 24, 2022
The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

TimeSformer This is an official pytorch implementation of Is Space-Time Attention All You Need for Video Understanding?. In this repository, we provid

Facebook Research 1k Dec 31, 2022
Distinguishing Commercial from Editorial Content in News

Distinguishing Commercial from Editorial Content in News In this repository you can find the following: An anonymized version of the data used for my

Timo Kats 3 Sep 26, 2022
Quickly and easily create / train a custom DeepDream model

Dream-Creator This project aims to simplify the process of creating a custom DeepDream model by using pretrained GoogleNet models and custom image dat

55 Dec 27, 2022
TiP-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

TiP-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling This is the official code release for the paper 'TiP-Adapter: Training-fre

peng gao 189 Jan 04, 2023
A simplified framework and utilities for PyTorch

Here is Poutyne. Poutyne is a simplified framework for PyTorch and handles much of the boilerplating code needed to train neural networks. Use Poutyne

GRAAL/GRAIL 534 Dec 17, 2022
This repository contains code from the paper "TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network"

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network This repository contains code from the paper "TTS-GAN: A Transformer-based Tim

Intelligent Multimodal Computing and Sensing Laboratory (IMICS Lab) - Texas State University 108 Dec 29, 2022
Adversarial Attacks are Reversible via Natural Supervision

Adversarial Attacks are Reversible via Natural Supervision ICCV2021 Citation @InProceedings{Mao_2021_ICCV, author = {Mao, Chengzhi and Chiquier

Computer Vision Lab at Columbia University 20 May 22, 2022
Data-driven reduced order modeling for nonlinear dynamical systems

SSMLearn Data-driven Reduced Order Models for Nonlinear Dynamical Systems This package perform data-driven identification of reduced order model based

Haller Group, Nonlinear Dynamics 27 Dec 13, 2022
A solution to the 2D Ising model of ferromagnetism, implemented using the Metropolis algorithm

Solving the Ising model on a 2D lattice using the Metropolis Algorithm Introduction The Ising model is a simplified model of ferromagnetism, the pheno

Rohit Prabhu 5 Nov 13, 2022
Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions This repo contains the dataset and code for the paper Benchmarking Ro

Jiachen Sun 168 Dec 29, 2022
This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti

Albert Webson 64 Dec 11, 2022
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators

Pandas TA - A Technical Analysis Library in Python 3 Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package

Kevin Johnson 3.2k Jan 09, 2023
Realtime_Multi-Person_Pose_Estimation

Introduction Multi Person PoseEstimation By PyTorch Results Require Pytorch Installation git submodule init && git submodule update Demo Download conv

tensorboy 1.3k Jan 05, 2023
Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph

Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph Model Description Open-CyKG is a framework that is constructed using an attenti

Injy Sarhan 34 Jan 05, 2023
Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Explaining in Style: Official TensorFlow Colab Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang, Yossi Gandelsman,

Google 197 Nov 08, 2022
Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021.

PHDimGeneralization Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021. Overvie

Tolga Birdal 13 Nov 08, 2022
Spatial Transformer Nets in TensorFlow/ TensorLayer

MOVED TO HERE Spatial Transformer Networks Spatial Transformer Networks (STN) is a dynamic mechanism that produces transformations of input images (or

Hao 36 Nov 23, 2022
Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image (ICCV 2021)

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color

75 Dec 02, 2022
Emblaze - Interactive Embedding Comparison

Emblaze - Interactive Embedding Comparison Emblaze is a Jupyter notebook widget for visually comparing embeddings using animated scatter plots. It bun

CMU Data Interaction Group 77 Nov 24, 2022