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Dataset results
17 results for “PyTorch”
PyTorch geometric datasets for morphVQ models
<p>The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset, we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation and area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit.</p>
PyTorch geometric datasets for morphVQ models
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PyTorch model for frame classification
<p>This is a trained PyTorch model for classifying a DNA sequence's (preferably of length 300) frame within an ORF.</p>
Open-Unmix-Pytorch UMX-L
<p>Weights of the UMX-L (umxl) music separation model for PyTorch. For more information, visit https://github.com/sigsep/open-unmix-pytorch</p>
Data from: Torchtree: flexible phylogenetic model development and inference using PyTorch
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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>
PyTorch model for taxonomic classification
<p>This is a trained PyTorch model for classifying an amino acid sequence's (preferably of length 100) taxonomic domain as viral (class 0), bacterial (class 1) or mammalian (class 2).</p> <p> </p>
Processed PyTorch Geometric dataset (out of date)
<p>PyTorch dataset processed from<a href="https://zenodo.org/record/7064665"> </a><a href="https://zenodo.org/record/6908291">https://zenodo.org/record/6908291</a></p>
Processed PyTorch Geometric dataset
<p>PyTorch dataset processed from https://zenodo.org/record/6911930</p>
ai-matrix BERT_PyTorch
<p>ai-matrix BERT_PyTorch pretrained model, converted from TensorFlow model, <strong>uncased_L-24_H-1024_A-16</strong></p>
Copper Import PyTorch
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Open-Unmix-Pytorch LabelNoise
<p>SDXDB21 LabelNoise Baseline</p> <pre>We split the training data into train and valid. For valid, the following songs were used:</pre> <pre>bc1f2967-f834-43bd-aadc-95afc897cfe7 cc3e4991-6cce-40fe-a917-81a4fbb92ea6 ed90a89a-bf22-444d-af3d-d9ac3896ebd2 f4b735de-14b1-4091-a9ba-c8b30c0740a7 bc964128-da16-4e4c-af95-4d1211e78c70 cc7f7675-d3c8-4a49-a2d7-a8959b694004 f40ffd10-4e8b-41e6-bd8a-971929ca9138</pre> <pre> The following commands were used to create the models:</pre> <pre><code class="language-bash">OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=3 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file vocals.wav \ --interferer-files bass.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=4 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file bass.wav \ --interferer-files vocals.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=5 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file drums.wav \ --interferer-files bass.wav vocals.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=7 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file other.wav \ --interferer-files bass.wav drums.wav vocals.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap</code></pre> <p> </p>
Open-Unmix Pytorch Bleeding
<p>SDXDB21 Bleeding Baseline</p> <pre>We split the training data into train and valid. For valid, the following songs were used:</pre> <pre>bc1f2967-f834-43bd-aadc-95afc897cfe7 cc3e4991-6cce-40fe-a917-81a4fbb92ea6 ed90a89a-bf22-444d-af3d-d9ac3896ebd2 f4b735de-14b1-4091-a9ba-c8b30c0740a7 bc964128-da16-4e4c-af95-4d1211e78c70 cc7f7675-d3c8-4a49-a2d7-a8959b694004 f40ffd10-4e8b-41e6-bd8a-971929ca9138</pre> <pre>The following commands were used to create the models:</pre> <pre><code class="language-bash">OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=3 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file vocals.wav \ --interferer-files bass.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=4 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file bass.wav \ --interferer-files vocals.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=5 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file drums.wav \ --interferer-files bass.wav vocals.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=7 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file other.wav \ --interferer-files bass.wav drums.wav vocals.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap</code></pre> <p> </p>
GORA-PT: Gradient-based Optimization of Resource Allocation of progeny in breeding schemes with PyTorch
<p>This directory is a public directory to share the scripts, simulated datasets, and results for the paper "Differentiable breeding: Automatic differentiation enables efficient gradient-based optimization of breeding strategies". In the "BSP" folder, we shared the core codes for the differentiable breeding schemes implemented by the automatic differentiation function of PyTorch. In the "scripts" folder, we shared the codes to perform gradient-based and black-box-based optimizations. In the "midstream" folder, we shared the datasets simulated by R and all the results, including the optimized results by PyTorch.</p>
Model weights for Lymphoid Aggregates Segmentation (in Pytorch 1.0.1)
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Validation data for tttsa, automated tilt-series alignment in PyTorch
<p>This dataset contains a tilt-series ordered by tilt angle saved in mrc format (.st), and the corresponding tilt angles for the collection (.rawtlt). The approximate tilt-axis angle was -88.7 in the serial-em mdoc file.</p> <p>The dataset also contains a bash script to run AreTomo (v1.3.4) for the same tilt-series and an output alignment from AreTomo (.aln).</p>
Project-NN-Pytorch-scripts Datasets and Resources
<p>=== General ===</p> <p>This Zenodo repository contains datasets used for https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts.</p> <p>All the data released with a CC BY-SA 4.0 lincense https://creativecommons.org/licenses/by-sa/4.0/, unless it is specified in the corresponding data package. </p> <p>Note:</p> <ol> <li>Data derived from ASVspoof 2019 LA https://datashare.ed.ac.uk/handle/10283/3336, provided by ASVspoof organization committee, is licensed under the Open Data Commons Attribution License https://opendatacommons.org/licenses/by/1-0/;</li> <li>Data derived from ASVspoof 2021 DF <a href="https://doi.org/10.5281/zenodo.4835107">10.5281/zenodo.4835107</a>, provided by ASVspoof organization committee, is licensed under the Open Data Commons Attribution License https://opendatacommons.org/licenses/by/1-0/;</li> <li>Data derived from ASVspoof 2021 LA <a href="https://doi.org/10.5281/zenodo.4817650">10.5281/zenodo.4817650</a>, provided by ASVspoof organization committee, is licensed under the Open Data Commons Attribution License https://opendatacommons.org/licenses/by/1-0/;</li> <li>ESPNet samples were partially produced based on the LJspeech database by Keith Ito and Linda Johnson, https://keithito.com/LJ-Speech-Dataset, released in the public domain. </li> <li>ESPNet samples were partially produced based on the LibriTTS database, LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech", Heiga Zen, Viet Dang, Rob Clark, Yu Zhang, Ron J. Weiss, Ye Jia, Zhifeng Chen, and Yonghui Wu, arXiv, 2019 https://www.openslr.org/60/. It is released with CC BY 4.0 https://creativecommons.org/licenses/by/4.0</li> </ol> <p>That's all</p> <p> </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.