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61 results for “Deep learning classification”
Dataset for: Deep learning and infrared thermography for asphalt pavement crack severity classification
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Deep learning and infrared thermography for asphalt pavement crack severity classification." Automation in Construction 140 (2022): 104383. https://doi.org/10.1016/j.autcon.2022.104383. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
<p>This Zenodo record contains the dataset and model weights for "Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing".</p> <p> </p> <p>This repository contains the following files:</p> <ul> <li> <p>For the human dataset by Wang et al.:</p> <ul> <li> <p>train_val_test_split.csv containing the mapping of the correct peptide by MaxQuant to either train, validation or test set</p> </li> <li> <p>psms_train_val_test.csv containing the mapping of correct PSMs (scan number, raw file and correct peptide by MaxQuant) to either train, validation or test set</p> </li> <li> <p>updated_spectralis_test_out.csv as before containing Spectralis-EA predictions and scores on test set, as well as initial peptides and scores by Casanovo and Novor and now containing also correct peptides by MaxQuant and Spectralis-scores on the combination of Casanovo and Novor sequences (column named spectralis_score_onlyRescoring)</p> </li> <li> <p>spectralis_test_out_heart_analysis.csv subset of 20220822_spectralis_test_out.csv containing only PSMs for the tissue heart with the computation of precision and recall values</p> </li> <li> <p>spectralis_test_out_pointnovo_deepnovo.csv containing predictions by DeepNovo and PointNovo with original scores and Spectralis-score, as well as correct peptides by MaxQuant</p> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>For the nine-species dataset by Tran et al.:</p> <ul> <li> <p>spectralis_ninespecies_out.csv containing spectrum identifiers, correct peptides by PEAKSDB, predicted peptides by the different de novo sequencing tools as well as original scores and Spectralis-scores for the different PSMs.</p> </li> </ul> </li> </ul>
Training and test data for: Not getting in too deep: A practical deep learning approach to routine crystallisation image classification
<p>These data were used to classify crystallisation experiments in Milne et al., (<a href="https://doi.org/10.1101/2022.09.28.509868">https://doi.org/10.1101/2022.09.28.509868</a>). Here, four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources were compared. It was shown that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative (Bruno et al. PLOS one, 13(6), 2018). Eight classes were used to rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions.</p>
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
<p>EuroSAT is a land use and land cover classification dataset. The dataset is based on Sentinel-2 satellite imagery covering 13 spectral bands and consists of 10 LULC classes with a total of 27,000 labeled and geo-referenced images. The dataset is associated with the publications "<a href="https://ieeexplore.ieee.org/abstract/document/8519248">Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>" and "<a href="https://ieeexplore.ieee.org/abstract/document/8736785">EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>".</p> <p>EuroSAT_RGB.zip contains the RGB version of the dataset, which includes the optical R, G and B frequency bands encoded as JPEG images.</p> <p>EuroSAT_MS.zip contains the multi-spectral version of the EuroSAT dataset, which includes all 13 Sentinel-2 bands in the original value range.</p>
SlumberNet: Deep learning classification of sleep stages using residual neural networks
<p>Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of accuracy (~98%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~50x faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet demonstrates the power of deep learning in sleep research, and looking forward, SlumberNet could be adapted to human EEG analysis and sleep stage classification. Thus, SlumberNet could be a valuable tool in understanding both sleep physiology and disorders in mammals.</p>
Detection and Classification of Diabetic Retinopathy From Posterior Pole Images With A Deep Learning Model
ClinicalTrials.gov study NCT04805541. IPD Sharing: NO. Countries: 1. Publications: 2.
Training and test data for: Not getting in too deep: A practical deep learning approach to routine crystallisation image classification
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Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers
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Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning
<p><em>This repository contains all code -from data collection to perceived mental workload classification- used in the "Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning" research and serves as supplementary material. <strong>A clear README file is provided, please refer to that for information about the individual scripts and their usage. </strong></em></p> <p>Mental workload detection has been attempted using various bio-signals. Recently, deep learning has allowed for novel methods and results within the BCI community. However, studies currently often only use a single modality to classify mental workload, whereas a plethora of modalities have proven to be valuable in this task.</p> <p>A dataset on which these scripts was also made publicly available under the following DOI: 10.4121/12932801<br> This dataset contains data collected during research into mental workload (MWL) detection using deep learning. It is being made public as supplementary data for publications, as well as for reuse in research that seeks to classify MWL using multimodal physiological data. The goal of this repository and dataset is to serve as a testing ground for the creation of deep neural networks that can classify MWL using multimodal physiological data.</p> <p>The data in this dataset was collected in the Behavioural, Management, and Social Sciences Lab, University of Twente, Enschede, The Netherlands in June/July 2020.</p>
PyTorch deep learning models for landscape classification (PyLC)
<pre><em>Pytorch pretrained models for use by the Python Landscape Classification Tool (PyLC) </em><em> Reference: An evaluation of deep learning semantic segmentation </em><em> for land cover classification of oblique ground-based photography, </em><em> MSc. Thesis 2020. </em><em> <http://hdl.handle.net/1828/12156> </em><em>Spencer Rose <spencerrose@uvic.ca>, June 2020 </em><em>University of Victoria</em></pre>
Data from: Eigenfeature-Enhanced Deep Learning: Advancing Tree Species Classification in Mixed Conifer Forests with Lidar
<p>Data and code used in <i>Lidar-derived eigenfeatures improve species classification using deep-learning in Southwestern mixed conifer forests</i>. Lidar data is provided for UAV-LS and ALS in <i>lidar_data.zip</i>. Las files are named by site and plot (e.g., 3-2). Field data, shapefiles, and code is provided in <i>data.zip</i>. R code is provided for tree segmentation, tree matching, image creation, and neural network cross validation. </p>
Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
<p>Complex bulk samples of insects from biodiversity surveys present a challenge for taxonomic identification, which could be overcome by high-throughput imaging combined with machine learning for rapid classification of specimens. These procedures require that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. However, such transfer learning may be problematic for the study of new samples not previously encountered in an image set, e.g. from unexplored ecosystems, and require methods of domain adaptation that reduce the differences in the feature distribution of the source and target domains (training and test sets). We assessed the efficiency of domain adaptation for family-level classification of bulk samples of Coleoptera, as a critical first step in the characterisation of biodiversity samples. Neural network models trained with images from a global database of Coleoptera were applied to a biodiversity sample from understudied forests in Cyprus as the target. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images and on dataset complexity. The accuracy of between-datasets predictions (across disparate source-target pairs that do not share any species or genera) was at most 82% and depended greatly on the standardisation of the imaging procedure. Algorithms for domain adaptation significantly improved the prediction performance of models trained by non-standardised, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, but the imaging conditions and classification algorithms need careful consideration.</p>
A Deep Learning Approach for Tomato Ripeness Classification
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Deep learning empowered sensor fusion boosts infant movement classification
<p>This repository contains data set and code for the paper:</p> <p>Kulvicius, T., Zhang, D., Poustka, L., Bölte, S., Jahn, L., Flügge, S., Kraft, M., Zweckstetter, M., Nielsen-Saines, K., Wörgötter, F., and Marschik, P. B. (2024). Deep learning empowered sensor fusion boosts infant movement classification. Communications Medicine, 5(16). DOI:10.1038/s43856-024-00701-w.</p> <p>For more details please see README.md file.</p>
Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance <Dataset>
<p>This is the dataset of manuscript entitled "Explainable Deep Learning for Automatic Rock Classification: High Accuracy Does Not Mean Great Model Performance". The manuscript is currently under review. Full access of this dataset will be released once the manuscript is accepted.</p>
The classification of cultural heritage buildings in Athens using deep learning techniques
<p>Dataset of images from buildings for classification tasks.</p>
Deep Learning-Based Recommendation System: Systematic Review and Classification - Outputs
<p>The datasets provided are the outputs of the paper titled "Deep Learning-Based Recommendation System: Systematic Review and Classification." They encompass multiple outputs, including primary articles, domain-focused articles, technique mapping, and domain mapping for each category.</p>
Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images
<p>This Dataset contains the train and test NLO images in pickle format used for the following publication: Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images</p>
Generalizable electroencephalographic classification of Parkinson's disease using deep learning
<p>This is the data originally accessed from http://predict.cs.unm.edu/downloads.php </p> <p>Here, we provide the pre-processed data from our manuscript to allow for the easy replication of our experiments. </p> <p>Here is the repo associated with our paper: https://github.com/diamandis-lab/CNN_EEG_PD </p>
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
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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.