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173 results for “convolutional neural networks”
Fig. 1 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 1. Dorsal habitus photos of males and females of Tuxedo spp., Pygovepres vaccinicola, and Phallospinophylus setosus generated for and used in this study.
Fig. 4 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 4. Validation accuracy and accuracy on test data for SVM linear classifier (A, B) and DNN approaches (C, D) for the three datasets.
Fig. 3 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 3. Identification accuracy for the male (top) and female (bottom) Tuxedo dataset for the five selected resolutions and the individual blocks 1–5 and the concatenated block.
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
<p>This repository includes the code and all relevant files used throughout our study. These encompass everything from the initial sheets of the whole acoustic dataset utilised for selecting the annotated dataset to the notebook (.ipynb) and the recordings employed in training the model.</p> <p>The .wav file are compressed in the file: <a href="../api/records/11220741/draft/files/wind-noise-detection-main.7z/content" target="_blank" rel="noopener noreferrer">wind-noise-detection-main.7z</a>/data/208_file_recordings_paper_wind.tar</p> <p> </p> <p> </p>
PLANT SPECIES RECOGNITION USING LEAF IMAGES AND CONVOLUTIONAL NEURAL NETWORKS (CAAR dataset, version 1)
<p>The CAAR dataset contains leaf images from plants obtained from the Arboreal Collection at Augusto Ribas Agricultural College (CAAR/UEPG). This plant collection is situated in Augusto Ribas College, located at the Ponta Grossa State University, Ponta Grossa, Paraná, Brasil. The images were taken using a smartphone camera with a resolution of 1659 x 2658 pixels and 24 bits of color depth. For each plant, images samples were collected using a white paper sheet background, varying the leaf orientation. The<br>number of plant species used to build the dataset is equal to 35. Data augmentation, using rotation and zoom, were used to increase the data size from 730 to 1986 images in this dataset.</p>
Achieving Peak Performance: Unveiling the Best Hyperparameter Combination for Accurate Skin Disorders Pose Classification with Convolutional Neural Network
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
Convolutional Neural Networks and their Activations: An Exploratory Case Study on Mounded Settlements
<p>This data set represents the output of our paper "Convolutional Neural Networks and their Activations: An Exploratory Case Study on Mounded Settlements". It comprises png images of 400x800 pixels. Half of the image represents a section of CORONA imagery where a site (files named Site_.png) or no site (files named NonSite_.png) is present. The other half of the image contains the activation map produced by various explainability techniques.</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Image data used for publication "Species-level image classification with convolutional neural network enable insect identification from habitus images "
<p>Image-crops of specimens from insect drawers</p> <p>In 2017 we scanned 208 insect drawers containing the collection of british carabids from the Natural History Museum London and extracted crops from the scanned images. This database contain 63.364 specimens that we used to train, validate and test a convolutional neural network.</p> <p>Each folder is named as the gbif id number. E.g. Carabus problematicus is 4470555: https://www.gbif.org/species/4470555</p>
Convolutional-neural-network-based reflection full-waveform inversion
<p>The data is used by the paper "Convolutional-neural-network-based reflection 1 full-waveform inversion"</p>
Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis
<p>Relevant data for the publication titled: Objective assessment of the relationship between quantified neuron morphological features and convolutional neural network image analysis</p>
Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks
<p>Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks" submitted to the journal "Artificial Intelligence for the Earth Systems" of the American Meteorological Society.</p>
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics [Dataset]
<p>Supplementary data for 'Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics'. </p>
Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network
<p>This repository contains:</p> <ul> <li>openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data.</li> <li>out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.</li> </ul>
Multi-scale full waveform inversion based on a convolutional neural network
<p>The research data from this paper are uploaded here and are available for download.</p>
Learning a quantum computer's capability using convolutional neural networks
<p>This is supplemental data and code for: D. Hothem et al., <em>Learning a quantum computer's capability using convolutional neural networks, </em>(to be published).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>Please direct any questions to Daniel Hothem (dhothem@sandia.gov).</p> <p>NOTE: This description template was borrowed from Timothy Proctor's Zenodo entry for: Scalable Randomized Benchmarking of Quantum Computers using Mirror Circuits.</p>
Dataset for: Star Photometry for DECaLS and SDSS Images Based on Convolutional Neural Networks
<p>The dataset consists of two parts, the training dataset and the comparison dataset. The training dataset and the comparison dataset are each divided into three parts, namely the simulation dataset, the SDSS dataset and the DECaLs dataset.</p> <p>The simulation dataset is simulated by PhoSim software and the original size of the simulation data is 512x512 pixels. The SDSS dataset is from DR12 and the ObjId of the target as well as other parameters are given in the csv file.The DECaLs dataset is from DR9.</p>
Data for "Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity"
<p>The trained 600-member ensemble convolutional neural networks (CNNs) for seasonal tropical cyclone (TC) activity to allow future studies. Please refer Fu et al. (2023; <em>Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity</em>) for more details.</p>
Assessing the Precision of Convolutional Neural Networks for Dental Age Estimation From Panoramic Radiographs
ClinicalTrials.gov study NCT05901857. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
ClinicalTrials.gov study NCT06796283. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.