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921 results for “neural networks”
M-Stock: AI-Based Photography Assessment System using Convolutional Neural Networks
<p>M-Stock (Mae Fah Luang University Photo Stock), an AI-driven automated photo evaluation platform designed to support student learning in photography by providing real-time feedback on both technical and artistic elements of their work. Using Convolutional Neural Networks (CNNs)</p> <p>Author: Assistant Professor Surapol Vorapatratorn, Ph.D<br>Organization: Center of Excellence in Artificial Intelligence and Emerging Technologies <br>School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand</p> <p><br>To start web service<br>1.Run runStreamlit.bat</p> <p>File description<br>Home.py => Home page<br>web.config => Streamlit's Path<br>Train_800.ipynb => Training model in Jupyter notebook file</p> <p>Directory description<br>image => Website image<br>model => model location<br>pages => each python web page <br>temp_dir => user upload file location<br>train_dir => training set</p>
Data for Deriving WMO cloud classes from ground-based RGB pictures with a residual neural network ensemble
Open the record for dataset details and reuse information.
Dataset for the article Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies
<p>Training and validation data sets along with the corresponding trained convolutional neural network in the article "Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies" by Sergio Baselga published in <em>Appl. Sci.</em> <strong>2024</strong>, <em>14</em>(23), 10859; https://doi.org/10.3390/app142310859</p>
Fortran/Python Interface in ARP-GEM1: Online Test of Neural Network Deep Convection
<p>Manuscript under review in AIES. Supporting Code and Dataset. </p> <p><strong>Abstract.</strong></p> <p>In this study, we present the integration of a neural network-based parameterization into the global atmospheric model ARP-GEM1, leveraging the Python interface of the OASIS coupler. This approach facilitates the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component responsible for neural network inference. As a proof-of-concept experiment, we trained a neural network to emulate the deep convection parameterization of ARP-GEM1. Using the flexible Fortran/Python interface, we have successfully replaced ARP-GEM1's deep convection scheme with a neural network emulator. To assess the performance of the neural network deep convection scheme, we have run a 5-years ARP-GEM1 simulation where the neural network replaced ARP-GEM1's deep convection parameterization. The evaluation of averaged fields showed good agreement with output from an ARP-GEM1 simulation using the physics-based deep convection scheme. The Python component was deployed on a separate partition from the general circulation model, using GPUs to increase inference speed of the neural network.</p>
Data for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes"
<p>This is sampling data of "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes".</p> <p>'qv_nn_in' for the large scale specific humidity, [kg/kg]<br> 'T_nn_in' for the large scale temperature, [K]<br> 'dqvls_nn_in' for the large scale moisture advection, [kg/kg/s]<br> 'dTls_nn_in' for the large scale moisture advection, [K/s]<br> 'qtend_check' for the moistening rate by CRM, [kg/kg/s]<br> 'stend_check' for the heating rate by CRM, [K/s]<br> 'SOLS' for direct shorwave solar radiation down to surface, [W/m2]<br> 'SOLSD' for diffusive shortwave solar radiation down to surface, [W/m2]<br> 'SOLL' for direct near infrared solar radiation down to surface, [W/m2]<br> 'SOLLD' for diffusive near infrared solar radiation down to surface, [W/m2]<br> 'SOLIN' for insolation at model top, [W/m2]<br> 'FSNS' for net shortwave radiation at model surface, [W/m2]<br> 'FSNT' for net shortwave radiation at model top, [W/m2]<br> 'FLNS' for net longwave radiation at model surface, [W/m2]<br> 'FLNT' for net longwave radiation at model top, [W/m2]<br> 'SPPS' for surface pressure, [Pa]</p> <p>To download the full dataset of the SPCAM simulation in 1998. Please click the dropbox link: https://www.dropbox.com/s/p841v1tw00rokdy/SPCAM_VAR_1998.tar.gz?dl=0</p>
Dataset for "Face Detection in Untrained Deep Neural Networks"
<p><strong>Dataset for</strong></p> <p><strong>"Face Detection in Untrained Deep Neural Networks"</strong></p> <p>Seungdae Baek, Min Song, Jaeson Jang, Gwangsu Kim, and Se-Bum Paik*</p> <p>*Contact: <a href="mailto:sbpaik@kaist.ac.kr">sbpaik@kaist.ac.kr</a></p> <p> </p> <p>To run demo codes for "Face Detection in Untrained Deep Neural Networks", please download files below.</p> <p> </p> <p><strong>1. Stimulus.zip</strong></p> <p>- <strong>IMG_cntr_210521.mat</strong> : A low-level feature-controlled stimulus set was used to find units that responded selectively to face images (Stigliani, 2015). Specifically, 260 images were prepared for each class (face, hand, horn, flower, chair, and scrambled face).</p> <p>- <strong>IMG_var_pos/size/rot_210521.mat</strong> : To investigate the invariance of face-selective units to face images of various sizes, positions, and rotation angles, the image set (Stigliani, 2015) was generated after modifying the size, position, and rotation angle of the faces and other objects in the low-level feature-controlled stimulus set.</p> <p>- <strong>IMG_var_view_210106.mat</strong> : This set was used to find units that invariantly responded to face images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-90°, -45°, 0°, 45°, 90°) with ten different faces obtained from (Gourier 2004)</p> <p> </p> <p><strong>2. Data.zip</strong></p> <p>- <strong>Data_PFI_XDream/RevCorr_ClsUnit.mat</strong> : The obtained preferred feature images, using reverse-correlation method and X-Dream, of units selective to each object class.</p> <p>- <strong>Data_SVM_NeuronType.mat</strong> : Simulated face detection performance using distinct types of selective units in Conv5 and using the shuffled responses of face-selective units in untrained networks (a. all selective units, b. units selective to non-face classes (nonface-selective), c. face-selective units, d. units selective to none of these classes (non-selective)).</p> <p>- <strong>Data_SVM_invariance.mat</strong> : Face detection performance with variation of the low-level features. </p> <p>- <strong>PretrainedNet</strong> : Sample networks was untrained and trained with three types of image sets (a. face-reduced ImageNet, b. original ImageNet, c. original ImageNet with added face images (Stigliani, 2015)) (number of networks = 3). </p> <p> - Net_Untrained : untrained AlexNets<br> - Net_FD_ImageNet : AlexNets trained to face-reduced ImageNet<br> - Net_ImageNet : AlexNets trained to original ImageNet<br> - Net_ImageNetwFace : AlexNets trained to original ImageNet with added face images</p> <p>- <strong>Data_Trained.mat</strong> : Result summary of four types of networks above (number of networks = 10).</p> <p> </p>
Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference [Artefact Evaluation]
<p>Source code of paper "Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference" submitted to FPGA'22 for artefact evaluation.</p>
AHI-CALIOP Collocated Data for Training and Validation of Cloud Masking Neural Networks
<p>Collocated data between AHI at 2km resolution (nadir) and CALIOP 1km cloud product v4.20 used for training and validating cloud identification neural networks. The main training and validation data from 2019 is stored in monthly directories, whilst the collocated dataset used to compare the NN, JMA and BoM cloud mask performances is the file "superdf.h5". All collocated data is stored as .h5 files and was built using the Python Pandas package. In this archive, the data has been stored as compressed directories for each month or as a single compressed file in the case of "superdf.h5" using tar with bzip2 compression or just bzip2 compression respectively.</p>
Data sets used in "Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model"
<p>The training, validation, and testing data sets for toluene, dodecane, and alpha-pinene models described in the manuscript. A link to the manuscript will be added here when it becomes available. All trajectories in the data sets were generated using GECKO-A. The source code for using the data sets can be found at https://github.com/NCAR/gecko-ml </p>
Neural network for forecasting egg abundance of the cabbage looper on three cole crops
<p>Neural network for predicting eggs of <em>Trichoplusia ni</em> in plants of brassica varieties using weather data (mean temperature, rainfall, and relative humidity from 15 prior), host plant (broccoli, cabbage, and cauliflower), and days after transplanting as input variables.</p>
Quantifying the spatial homogeneity of urban road networks via graph neural networks
<p>Publication: Quantifying the spatial homogeneity of urban road networks via graph neural networks, Nature Machine Intelligence, 2022.</p> <p>Publication DOI: 10.1038/s42256-022-00462-y</p> <p>Please refer to https://github.com/jiang719/road-network-predictability.</p>
Pre-trained neural network model for pruning example code
<p>Pre-trained neural network models for example codes of neural network pruning.</p> <p>Example pruning codes are published in "https://github.com/FujitsuResearch/automatic_pruning".</p>
Convolutional-neural-network-based reflection-waveform inversion (major revision)
<p>This dataset is used to plot the synthetic results shown in paper: "Convolutional-neural-network-based reflection-waveform inversion"</p>
Source data for "In-degree centrality in a social network is linked to coordinated neural activity"
<p>The following includes the source data for the manuscript titled "In-degree centrality in a social network is linked to coordinated neural activity". A second version of the source data ("Source Data_updated_011222.xlsx") includes the source data for the figures in the supplementary materials.</p>
Topographic deep neural networks predict the functional organization of the primate ventral visual pathway
<p>Recording of presentation at the Neuroscience 2021 annual meeting (held virtually). The abstract follows:</p> <p> </p> <p>The primate ventral visual pathway is organized into functional maps, including pinwheel-like arrangements of orientation-tuned neurons in primary visual cortex (V1) and patches of category-selective neurons in higher visual cortex. While deep convolutional neural networks (DCNNs) trained for object recognition accurately predict neural representations throughout the ventral pathway, they have no spatial layout for features at a given retinotopic location and are thus unable to predict the rich topographic organization of visual cortex. Here, we close this gap by first assigning each DCNN unit a position in a 2D cortical sheet, then training the network to minimize a cost function with two components: one encouraging accurate object recognition, and another favoring correlated responses among nearby units in each model layer (Figure 1A, 1B). </p> <p>We find that training with this composite spatial loss produces brain-like topographic maps in both early and later model layers (Figure 1B). Early layers contain smooth orientation preference maps with pinwheels, clusters of units preferring the same spatial frequency, and color-preference domains resembling V1 “blobs”. In a later layer of the same model, we observe clusters of category-selective units, e.g., face patches, whose spatial organization largely matches that found in primate higher visual cortex. Our model thus leverages local response correlations, which have been linked to theories of wire-length minimization, to accurately predict neuron responses and functional organization throughout the ventral visual pathway. In support of the wire-length minimization hypothesis, we find that our topographic DCNN would require shorter connections than a standard DCNN to support connections between similarly-tuned neurons within early (38% reduction) and later (31% reduction) model layers (Figure 1D). These results suggest that the functional organization of visual cortex can be explained by two constraints: the need to perform object recognition and pressure for local populations of neurons to have correlated responses.</p>
TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet
<p>We develop a Convolutional Neural Network, named SIFnet, that increases the spatial resolution of SIF from the TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations together with a broad range high resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.</p> <p>Temporal coverage: April 2018 until March 2021, 16 day time steps</p> <p>Data for other regions can be requested and produced by the authors. Please refer for further information to: </p> <p>Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., & Chen, J. (2022). A Convolutional Neural Network for Spatial Downscaling of Satellite-Based Solar-Induced Chlorophyll Fluorescence (SIFnet). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). <em>Biogeosciences</em>, <em>19</em>(6), 1777-1793. DOI: https://doi.org/10.5194/bg-19-1777-2022</p>
Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables - Version 2</p>
Neural Network Adaption for Depth Sensor Replication (Datasets)
<p>This repository contains the datasets used in the paper "Neural Network Adaption for Depth Sensor<br> Replication". They contain RGB-D data that was recorded using iPads where the Structure_Sensor set was recorded using an Occipital Structure Sensor while Apple_Lidar used the inbuilt iPad LiDAR sensor with enabled smoothing by ARKit. The Structure Sensor data is scaled so that the maximum value is 4 meters while the LiDAR data is scaled to 8 meters as it provides more accuracy at longer ranges.</p>
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
<p>The rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception ResNet v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.</p>
Shared Data for De-scattering Deep Neural Network
<p>These images show mouse cortical layer 2/3 pyramidal neurons sparsely labeled with a cell fill (eYFP or mScarlet-I) to visualize the dendritic arbor, including dendritic spines. Cells were imaged in vivo using point-scan two-photon microscopy (PSTPM) and temporal focusing microscopy (TFM). These images were used for training and testing the De-Scattering Deep Neural Network (<a href="https://github.com/eleweiz/DeScattering_NN">https://github.com/eleweiz/DeScattering_NN</a>).</p>
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.