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.

Sequence to Sequence Models with PyTorch

Sequence to Sequence models with PyTorch This repository contains implementations of Sequence to Sequence (Seq2Seq) models in PyTorch At present it ha

Sandeep Subramanian 708 Dec 19, 2022
The official GitHub repository for the Argoverse 2 dataset.

Argoverse 2 API Official GitHub repository for the Argoverse 2 family of datasets. If you have any questions or run into any problems with either the

Argo AI 156 Dec 23, 2022
An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data

GLOM TensorFlow This Python package attempts to implement GLOM in TensorFlow, which allows advances made by several different groups transformers, neu

Rishit Dagli 32 Feb 21, 2022
《Lerning n Intrinsic Grment Spce for Interctive Authoring of Grment Animtion》

Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation Overview This is the demo code for training a motion invariant enco

YuanBo 213 Dec 14, 2022
Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

tonne 1.4k Dec 29, 2022
Decorator for PyMC3

sampled Decorator for reusable models in PyMC3 Provides syntactic sugar for reusable models with PyMC3. This lets you separate creating a generative m

Colin 50 Oct 08, 2021
A curated list of awesome neural radiance fields papers

Awesome Neural Radiance Fields A curated list of awesome neural radiance fields papers, inspired by awesome-computer-vision. How to submit a pull requ

Yen-Chen Lin 3.9k Dec 27, 2022
[ICML 2021] Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data

Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data This repo provides the source code & data of our paper: Break-It-Fix-It: Unsupervised

Michihiro Yasunaga 86 Nov 30, 2022
LyaNet: A Lyapunov Framework for Training Neural ODEs

LyaNet: A Lyapunov Framework for Training Neural ODEs Provide the model type--config-name to train and test models configured as those shown in the pa

Ivan Dario Jimenez Rodriguez 21 Nov 21, 2022
Code for 'Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning', ICCV 2021

CMIC-Retrieval Code for Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning. ICCV 2021. Introduction In this wo

42 Nov 17, 2022
Tools for manipulating UVs in the Blender viewport.

UV Tool Suite for Blender A set of tools to make editing UVs easier in Blender. These tools can be accessed wither through the Kitfox - UV panel on th

35 Oct 29, 2022
A tensorflow implementation of an HMM layer

tensorflow_hmm Tensorflow and numpy implementations of the HMM viterbi and forward/backward algorithms. See Keras example for an example of how to use

Zach Dwiel 283 Oct 19, 2022
Modelisation on galaxy evolution using PEGASE-HR

model_galaxy Modelisation on galaxy evolution using PEGASE-HR This is a labwork done in internship at IAP directed by Damien Le Borgne (https://github

Adrien Anthore 1 Jan 14, 2022
Data and extra materials for the food safety publications classifier

Data and extra materials for the food safety publications classifier The subdirectories contain detailed descriptions of their contents in the README.

1 Jan 20, 2022
DNA-RECON { Automatic Web Reconnaissance Tool }

ABOUT TOOL : DNA-RECON is an automatic web reconnaissance tool written in python. This tool made for reconnaissance and information gathering with an

NIKUNJ BHATT 25 Aug 11, 2021
Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL

A Minimalist Approach to Offline Reinforcement Learning TD3+BC is a simple approach to offline RL where only two changes are made to TD3: (1) a weight

Scott Fujimoto 193 Dec 23, 2022
Sandbox for training deep learning networks

Deep learning networks This repo is used to research convolutional networks primarily for computer vision tasks. For this purpose, the repo contains (

Oleg Sémery 2.7k Jan 01, 2023
🛰️ Awesome Satellite Imagery Datasets

Awesome Satellite Imagery Datasets List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. Newest datase

Christoph Rieke 3k Jan 03, 2023
A task-agnostic vision-language architecture as a step towards General Purpose Vision

Towards General Purpose Vision Systems By Tanmay Gupta, Amita Kamath, Aniruddha Kembhavi, and Derek Hoiem Overview Welcome to the official code base f

AI2 79 Dec 23, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Jan 02, 2023