Find research datasets worth reusing
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datasets available to search
ShareScore release 0.9.0
Dataset results
40 results for “pretrained model”
Example data and pretrained Translatomer model
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Toward Universal Text-to-Music Retrieval (Pretrained Model)
<p>Pretrained model for Toward Universal Text-to-Music Retrieval. The following results are based on <a href="https://github.com/SeungHeonDoh/msd-subsets">MSD-ECAL</a> dataset pre-training.</p> <p>- github: https://github.com/SeungHeonDoh/music-text-representation <br> - Demo : https://seungheondoh.github.io/text-music-representation-demo/ <br> - ArXiv : update soon</p> <p> </p>
DECCS - Datasets and Pretrained Models
<p>Datasets and Pretrained Models for the paper Deep Clustering With Consensus Representations (DECCS).</p>
Textless Speech-to-Music Retrieval Using Emotion Similarity (Pretrained Model)
<p>- github: https://github.com/SeungHeonDoh/speech-to-music <br> - Demo : https://seungheondoh.github.io/speech-to-music-demo/<br> - ArXiv : update soon</p>
Dataset and Pretrained Models of the FBRestNet
<p>These files contain the image datasets used to train the FBRestNet model for 1D inverse Abel Integral reconstruction, as well as the different pretrained models.</p>
CproMG Dataset and Pretrained Models
<p>Dataset and pretrained model for CProMG</p>
TCRpred pretrained models
<p>Pretrained TCRpred models for TCR-pMHC interaction prediction</p>
Pretrained model for UCBShift
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ProtFound-V Pretrained Models and Features
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model of the PreTrained DeepLabV3+
<p>model of the PreTrained DeepLabV3+ </p>
Pretrained-Guided Conditional Diffusion Models for Microbiome Data Denoising
<p>All datasets and corresponding metadata used in mbVDiT.</p>
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
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OpenFWI Tutorial Pretrained Model and Figure
<p>OpenFWI Tutorial Pretrained Model and Figure</p>
Pretrained models of HamGNN and test examples
<p>The dataset contains the HamGNN models trained on carbon allotropes, silicon allotropes, SiO<sub>2</sub> isomers, and Bi<sub>x</sub>Se<sub>y</sub> compounds. The structures beyond the training dataset are utilized as the test examples for HamGNN.</p>
Pretrained model for tutorial from: "PartSeg v2: Bioimage segmentation using advanced Deep Learning techniques"
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Pretrained models and data for CC2Vec
<p>Data & Pretrained models for the experiments in the paper, CC2Vec: Distributed Representations of Code Changes.</p> <p> </p> <p>This research was supported by the Singapore National Research Foundation (Award number: NRF2016-NRFANR003) and the ANR ITrans project</p>
CA-SUM pretrained models
<p>This dataset contains <strong>pretrained models of the CA-SUM network architecture for video summarization</strong>, that is presented in our work titled “Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames”, in Proc. ACM ICMR 2022. </p> <p><strong>Method overview:</strong></p> <p>In our ICMR 2022 paper we describe a new method for unsupervised video summarization. To overcome limitations of existing unsupervised video summarization approaches, that relate to the unstable training of Generator-Discriminator architectures, the use of RNNs for modeling long-range frames' dependencies and the ability to parallelize the training process of RNN-based network architectures, the developed method relies solely on the use of a self-attention mechanism to estimate the importance of video frames. Instead of simply modeling the frames' dependencies based on global attention, our method integrates a concentrated attention mechanism that is able to focus on non-overlapping blocks in the main diagonal of the attention matrix, and to enrich the existing information by extracting and exploiting knowledge about the uniqueness and diversity of the associated frames of the video. In this way, our method makes better estimates about the significance of different parts of the video, and drastically reduces the number of learnable parameters. Experimental evaluations using two benchmarking datasets (SumMe and TVSum) show the competitiveness of the proposed method against other state-of-the-art unsupervised summarization approaches, and demonstrate its ability to produce video summaries that are very close to the human preferences. An ablation study that focuses on the introduced components, namely the use of concentrated attention in combination with attention-based estimates about the frames' uniqueness and diversity, shows their relative contributions to the overall summarization performance.</p> <p><strong>File format:</strong></p> <p>The “pretrained_models.zip“ file that is provided in the present zenodo page contains a set of pretrained models of the CA-SUM network architecture. After downloading and unpacking this file, in the created “pretrained_models” folder, you will find two sub-directories one per each of the utilized benchmarking datasets (SumMe and TVSum) in our experimental evaluations. Within each of these sub-directories we provide the pretrained model (.pt file) for each data-split (split0-split4), where the naming of the provided .pt file indicates the training epoch and the value of the length regularization factor of the selected pretrained model.</p> <p>The models have been trained in a full-batch mode (i.e., batch size is equal to the number of training samples) and were automatically selected after the end of the training process, based on a methodology that relies on transductive inference (described in Section 4.2 of [1]). Finally, the data-splits we used for performing inference on the provided pretrained models, and the source code that can be used for training your own models of the proposed CA-SUM network architecture, can be found at: <a href="https://github.com/e-apostolidis/CA-SUM">https://github.com/e-apostolidis/CA-SUM</a>.</p> <p><strong>License and Citation:</strong></p> <p>These resources are provided for academic, non-commercial use only. If you find these resources useful in your work, please cite the following publication where they are introduced:</p> <p>E. Apostolidis, G. Balaouras, V. Mezaris, and I. Patras. 2022, “Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames”, Proc. of the 2022 Int. Conf. on Multimedia Retrieval (ICMR ’22), June 2022, Newark, NJ, USA. <a href="https://doi.org/10.1145/3512527.3531404">https://doi.org/10.1145/3512527.3531404</a> Software available at: <a href="https://github.com/e-apostolidis/CA-SUM">https://github.com/e-apostolidis/CA-SUM</a></p>
Test Pretrained models for Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
<p>Test description</p>
pretrained model for TDiffDe
<p>Please put the model into /Checkpoints/HS/ </p>
SuMe data v8 pretrain all models
<p>Pretrain all data v8 models</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.