NeurIPS 2021 dataset used for benchmarking feature selection for integration in H5AD format. Files contain the full raw dataset, the processed batches used to create the reference and the processed batches used as a query.Note: These files have been saved with compression to reduce file size. Re-saving without compression will reduce reading times if needed.If used, please cite:Lance C, Luecken MD, Burkhardt DB, Cannoodt R, Rautenstrauch P, Laddach A, et al. Multimodal single cell data integration challenge: Results and lessons learned. In: Kiela D, Ciccone M, Caputo B, editors. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track. PMLR; 06--14 Dec 2022. p. 162–76. Available from: https://proceedings.mlr.press/v176/lance22a.htmlANDLuecken MD, Burkhardt DB, Cannoodt R, Lance C, Agrawal A, Aliee H, et al. A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). 2022 [cited 2022 Nov 8]. Available from: https://openreview.net/pdf?id=gN35BGa1Rt

NeurIPS 2025 Papers Dataset

This dataset contains all accepted papers from NeurIPS 2025, scraped from OpenReview.

  Dataset Statistics





  Overview

Total Papers: 5772 Unique Paper IDs: 5772 ✅ No duplicate IDs

  Track Distribution

Main Track: 5,275 papers (91.4%) Datasets and Benchmarks Track: 497 papers (8.6%)

  Award Distribution

Poster: 4,949 papers (85.7%) Oral: 84 papers (1.5%) Spotlight: 739 papers (12.8%)

  Track × Award… See the full description on the dataset page: https://huggingface.co/datasets/huyxdang/neurips-2025-papers.

Subset of the benchmark dataset published in Luecken et al. (2021).

Accepted by NeurIPS 2024 Datasets and Benchmarks Track

We introduce the RePair puzzle-solving dataset, a large-scale real world dataset of fractured frescoes from the archaelogical campus of Pompeii. Our dataset consists of over 1000 fractured frescoes. The RePAIR stands as a realistic computational challenge for methods for 2D and 3D puzzle solving, and serves as a benchmark that enables the study of fractured object reassembly and presents new challenges for geometric shape understanding. Please visit our website for more dataset information, access to source code scripts and for an interactive gallery viewing of the dataset samples.

This dataset is associated to submission 1335 at NeurIPS 2025 - Dataset and Benchmarks track. The benchmark is intended to be used with the proposed submission environments (see the source code). See the provided README for information about dataset downloading and running the evaluations.

This is the official data repository of the Data-Centric Image Classification (DCIC) Benchmark. The goal of this benchmark is to measure the impact of tuning the dataset instead of the model for a variety of image classification datasets. Full details about the collection process, the structure and automatic download at

Paper: https://arxiv.org/abs/2207.06214

Source Code: https://github.com/Emprime/dcic

The license information is given below as download.

Citation

Please cite as

@article{schmarje2022benchmark,
  author = {Schmarje, Lars and Grossmann, Vasco and Zelenka, Claudius and Dippel, Sabine and Kiko, Rainer and Oszust, Mariusz and Pastell, Matti and Stracke, Jenny and Valros, Anna and Volkmann, Nina and Koch, Reinahrd},
  journal = {36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks},
  title = {{Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation}},
  year = {2022}
}

Please see the full details about the used datasets below, which should also be cited as part of the license.

@article{schoening2020Megafauna,
author = {Schoening, T and Purser, A and Langenk{\"{a}}mper, D and Suck, I and Taylor, J and Cuvelier, D and Lins, L and Simon-Lled{\'{o}}, E and Marcon, Y and Jones, D O B and Nattkemper, T and K{\"{o}}ser, K and Zurowietz, M and Greinert, J and Gomes-Pereira, J},
doi = {10.5194/bg-17-3115-2020},
journal = {Biogeosciences},
number = {12},
pages = {3115--3133},
title = {{Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison}},
volume = {17},
year = {2020}
}

@article{Langenkamper2020GearStudy,
author = {Langenk{\"{a}}mper, Daniel and van Kevelaer, Robin and Purser, Autun and Nattkemper, Tim W},
doi = {10.3389/fmars.2020.00506},
issn = {2296-7745},
journal = {Frontiers in Marine Science},
title = {{Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification}},
volume = {7},
year = {2020}
}


@article{peterson2019cifar10h,
author = {Peterson, Joshua and Battleday, Ruairidh and Griffiths, Thomas and Russakovsky, Olga},
doi = {10.1109/ICCV.2019.00971},
issn = {15505499},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
pages = {9616--9625},
title = {{Human uncertainty makes classification more robust}},
volume = {2019-Octob},
year = {2019}
}

@article{schmarje2019,
author = {Schmarje, Lars and Zelenka, Claudius and Geisen, Ulf and Gl{\"{u}}er, Claus-C. and Koch, Reinhard},
doi = {10.1007/978-3-030-33676-9_26},
issn = {23318422},
journal = {DAGM German Conference of Pattern Regocnition},
number = {November},
pages = {374--386},
publisher = {Springer},
title = {{2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy}},
volume = {11824 LNCS},
year = {2019}
}

@article{schmarje2021foc,
author = {Schmarje, Lars and Br{\"{u}}nger, Johannes and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Kiko, Rainer and Koch, Reinhard},
doi = {10.3390/s21196661},
issn = {1424-8220},
journal = {Sensors},
number = {19},
pages = {6661},
title = {{Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy}},
volume = {21},
year = {2021}
}

@article{schmarje2022dc3,
author = {Schmarje, Lars and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Zelenka, Claudius and Kiko, Rainer and Stracke, Jenny and Volkmann, Nina and Koch, Reinhard},
journal = {Proceedings of the European Conference on Computer Vision (ECCV)},
title = {{A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering}},
year = {2022}
}


@article{obuchowicz2020qualityMRI,
author = {Obuchowicz, Rafal and Oszust, Mariusz and Piorkowski, Adam},
doi = {10.1186/s12880-020-00505-z},
issn = {1471-2342},
journal = {BMC Medical Imaging},
number = {1},
pages = {109},
title = {{Interobserver variability in quality assessment of magnetic resonance images}},
volume = {20},
year = {2020}
}


@article{stepien2021cnnQuality,
author = {St{\c{e}}pie{\'{n}}, Igor and Obuchowicz, Rafa{\l} and Pi{\'{o}}rkowski, Adam and Oszust, Mariusz},
doi = {10.3390/s21041043},
issn = {1424-8220},
journal = {Sensors},
number = {4},
title = {{Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment}},
volume = {21},
year = {2021}
}

@article{volkmann2021turkeys,
author = {Volkmann, Nina and Br{\"{u}}nger, Johannes and Stracke, Jenny and Zelenka, Claudius and Koch, Reinhard and Kemper, Nicole and Spindler, Birgit},
doi = {10.3390/ani11092655},
journal = {Animals 2021},
pages = {1--13},
title = {{Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys}},
volume = {11},
year = {2021}
}

@article{volkmann2022keypoint,
author = {Volkmann, Nina and Zelenka, Claudius and Devaraju, Archana Malavalli and Br{\"{u}}nger, Johannes and Stracke, Jenny and Spindler, Birgit and Kemper, Nicole and Koch, Reinhard},
doi = {10.3390/s22145188},
issn = {1424-8220},
journal = {Sensors},
number = {14},
pages = {5188},
title = {{Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks}},
volume = {22},
year = {2022}
}

Context

Dataset from NeurIPS2021 challenge similar to Kaggle 2022 competition: https://www.kaggle.com/competitions/open-problems-multimodal "Open Problems - Multimodal Single-Cell Integration Predict how DNA, RNA & protein measurements co-vary in single cells"

It is https://en.wikipedia.org/wiki/ATAC-seq#Single-cell_ATAC-seq single cell ATAC-seq data. And single cell RNA-seq data: https://en.wikipedia.org/wiki/Single-cell_transcriptomics#Single-cell_RNA-seq

Single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

(For companion dataset on CITE-seq = scRNA-seq + Proteomics, see: https://www.kaggle.com/datasets/alexandervc/citeseqscrnaseqproteins-challenge-neurips2021)

Particular data

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122

Expression profiling by high throughput sequencing Genome binding/occupancy profiling by high throughput sequencing Summary Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors. Half the samples were measured using the 10X Multiome Gene Expression and Chromatin Accessability kit and half were measured using the 10X 3' Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site. In the competition, participants were tasked with challenges including modality prediction, matching profiles from different modalities, and learning a joint embedding from multiple modalities.

Overall design Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors.

Contributor(s) Burkhardt DB, Lücken MD, Lance C, Cannoodt R, Pisco AO, Krishnaswamy S, Theis FJ, Bloom JM Citation https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/158f3069a435b314a80bdcb024f8e422-Abstract-round2.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Context

Dataset from NeurIPS2021 challenge similar to Kaggle 2022 competition: https://www.kaggle.com/competitions/open-problems-multimodal "Open Problems - Multimodal Single-Cell Integration Predict how DNA, RNA & protein measurements co-vary in single cells"

CITE-seq - joint single cell RNA sequencing + single cell measurements of CD** proteins. (https://en.wikipedia.org/wiki/CITE-Seq) (For companion dataset on scRNA-seq + scATAC-seq, see: https://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021 )

Single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122

Expression profiling by high throughput sequencing Genome binding/occupancy profiling by high throughput sequencing Summary Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors. Half the samples were measured using the 10X Multiome Gene Expression and Chromatin Accessability kit and half were measured using the 10X 3' Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site. In the competition, participants were tasked with challenges including modality prediction, matching profiles from different modalities, and learning a joint embedding from multiple modalities.

Overall design Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors.

Contributor(s) Burkhardt DB, Lücken MD, Lance C, Cannoodt R, Pisco AO, Krishnaswamy S, Theis FJ, Bloom JM Citation https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/158f3069a435b314a80bdcb024f8e422-Abstract-round2.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Datasets from the Paper LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite at NeurIPS 2023 Track on Datasets and Benchmarks.

This dataset contains the results of the review paper Measuring What Matters, presented at NeurIPS 2025 Datasets and Benchmarks Track.

The gene expression portion of the NeurIPS 2021 challenge 10x multiome dataset (Luecken et al., NeurIPS datasets and benchmarks track 2021), originally obtained from GEO. Contains single-cell gene expression of 69,249 cells for 13,431 genes. The adata.X field contains normalized data and adata.layers['counts'] contains raw expression values. We computed a latent space using scANVI (Xu et al., MSB 2021), following their tutorial.

🌲 TreeFinder: TreeFinder: A US-Scale Benchmark Dataset for Individual Tree Mortality Monitoring Using High-Resolution Aerial Imagery

Accepted to NeurIPS 2025 (Datasets & Benchmarks Track)

TreeFinder is the first large-scale, high-resolution benchmark dataset for mapping individual dead trees across the contiguous United States (CONUS). Built to advance computer vision methods for ecological monitoring and carbon assessment, TreeFinder provides pixel-level annotations of dead trees from high-resolution aerial imagery, enriched with ecological metadata and paired with performance benchmarks.

📦 What's in the Dataset?

• 1,000 Sites across 48 U.S. States
  • Spatially diverse sampling of forested regions across CONUS
• 23,000 Hectares of 0.6m NAIP Imagery
  • Aerial imagery from the National Agriculture Imagery Program (NAIP), including 4 channels (RGB + NIR)
• 20,000+ Manually Annotated Dead Trees
  • Pixel-level masks created through expert labeling and validated via multi-temporal image comparison
• ML-Ready Patches
  • Each raw scene is tiled into $224 \times 224$ patches for deep learning, with associated segmentation masks

🧠 Benchmark Models

We provide benchmark performance results using five semantic segmentation models, including: - U-Net and DeepLabV3+ (CNN-based) - ViT, SegFormer, and Mask2Former (Transformer-based) - DOFA (a multimodal foundation model trained on satellite data) Each model is trained and evaluated across various domain generalization settings (e.g., region, climate, forest type) to test robustness.

🗺️ Metadata & Scenarios

Each patch is enriched with: - Geographic Coordinates - Köppen–Geiger Climate Zone - Primary Tree Type (from USDA Forest Service maps)

These metadata enable benchmarking under challenging scenarios like: - Cross-region generalization (e.g., East → West) - Climate domain shifts - Forest type transfer

🧪 Why Use TreeFinder?

TreeFinder enables the development and evaluation of machine learning models for high-impact environmental tasks such as: - Forest health monitoring - Carbon flux modeling - Wildfire risk assessment It is designed to foster cross-disciplinary collaboration between the machine learning and Earth science communities by providing a reproducible, challenging, and ecologically grounded benchmark.

📚 Citation

If you use TreeFinder in your research, please cite the following paper:

Zhihao Wang, Cooper Li, Ruichen Wang, Lei Ma, George Hurtt, Xiaowei Jia, Gengchen Mai, Zhili Li, Yiqun Xie.
TreeFinder: A US-Scale Benchmark Dataset for Individual Tree Mortality Monitoring Using High-Resolution Aerial Imagery.
In Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS 2025), Datasets and Benchmarks Track, 2025.

🖥 MedSG-Bench: A Benchmark for Medical Image Sequences Grounding

📖 Paper | 💻 Code | 🤗 Dataset

🔥 MedSG-Bench is accepted at NeurIPS 2025 Datasets and Benchmarks Track as a Spotlight.

  MedSG-Bench

MedSG-Bench is the first benchmark for medical image sequences grounding. 👉 We also provide MedSG-188K, a grounding instruction-tuning dataset. 👉 MedSeq-Grounder, the model trained on MedSG-188K, is available here.

  Metadata

This dataset… See the full description on the dataset page: https://huggingface.co/datasets/MedSG-Bench/MedSG-Bench.

This collection includes four well-known datasets used to benchmark causal machine learning algorithms. The datasets are: IHDP [1], News [2], Twins [3], Jobs [4].References[1] J. L. Hill, ‘Bayesian Nonparametric Modeling for Causal Inference’, Journal of Computational and Graphical Statistics, vol. 20, no. 1, pp. 217–240, Jan. 2011, doi: 10.1198/jcgs.2010.08162.[2] F. D. Johansson, U. Shalit, and D. Sontag, ‘Learning representations for counterfactual inference’, in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, in ICML’16. New York, NY, USA: JMLR.org, Jun. 2016, pp. 3020–3029.[3] C. Louizos, U. Shalit, J. M. Mooij, D. Sontag, R. Zemel, and M. Welling, ‘Causal Effect Inference with Deep Latent-Variable Models’, Advances in Neural Information Processing Systems, vol. 30, 2017, Accessed: May 25, 2021. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/94b5bde6de888ddf9cde6748ad2523d1-Abstract.html[4] J. A. Smith and P. E. Todd, ‘Does matching overcome LaLonde’s critique of nonexperimental estimators?’, Journal of Econometrics, vol. 125, no. 1–2, pp. 305–353, 2005.

Dataset for "A Benchmark for Antimicrobial Peptide Recognition Based on Structure and Sequence Representation" at NeurIPS 2025 Dataset and Benchmark

InspiredFromHetionet

License: Creative Commons Attribution–NonCommercial 2.0 (CC BY-NC 2.0) This dataset is part of the When No Paths Lead to Rome benchmark for systematic neural relational reasoning,accepted for NeurIPS 2025 Datasets and Benchmarks Track.

Dataset accompanying the NeurIPS 2022 Dataset and Benchmark Track paper: Breaking Bad: A Dataset for Geometric Fracture and Reassembly. Please refer to our project page for more details.

License: The Breaking Bad dataset collects 3D meshes from ShapeNet and Thingi10K thus inheriting their terms of use. Please refer to ShapeNet and Thingi10K for more details. We release each model in our dataset with an as-permissive-as-possible license compatible with its underlying base model. Please refer to ShapeNet and Thingi10K for restrictions and depositor requirements of each model.

The benchmark code is available at: https://github.com/Junjue-Wang/LoveDA

Highlights:

5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan

Focus on different geographical environments between Urban and Rural

Advance both semantic segmentation and domain adaptation tasks

Three considerable challenges: multi-scale objects, complex background samples, and inconsistent class distributions

Reference:

@inproceedings{wang2021loveda, title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, editor = {J. Vanschoren and S. Yeung}, year={2021}, volume = {1}, pages = {}, url={https://datasets-benchmarks proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf} }

License:

The owners of the data and of the copyright on the data are RSIDEA, Wuhan University. Use of the Google Earth images must respect the "Google Earth" terms of use. All images and their associated annotations in LoveDA can be used for academic purposes only, but any commercial use is prohibited. (CC BY-NC-SA 4.0)

Original Dataset

https://zenodo.org/records/7105232

Citation

@inproceedings{prabhushankarolives2022,
title={OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics},
author={Prabhushankar, Mohit and Kokilepersaud, Kiran and Logan, Yash-yee and Trejo Corona, Stephanie and AlRegib, Ghassan and Wykoff, Charles},
booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 2 (NeurIPS Datasets and Benchmarks 2022) },
year={2022}
}

MatSeg Dataset and benchmark for zero-shot material state segmentation.

MatSeg Benchmark containing 1220 real-world images and their annotations is available at MatSeg_Benchmark.zip the file contains documentation and Python readers.

MatSeg dataset containing synthetic images with infused natural images patterns is available at MatSeg3D_part_*.zip and MatSeg3D_part_*.zip (* stand for number).

MatSeg3D_part_*.zip: contain synthethc 3D scenes

MatSeg2D_part_*.zip: contain syntethc 2D scenes

Readers and documentation for the synthetic data are available at: Dataset_Documentation_And_Readers.zip

Readers and documentation for the real-images benchmark are available at: MatSeg_Benchmark.zip

The Code used to generate the MatSeg Dataset is available at: https://zenodo.org/records/11401072

Additional permanent sources for downloading the dataset and metadata: 1, 2

Evaluation scripts for the Benchmark are now available at:

https://zenodo.org/records/13402003 and https://e.pcloud.link/publink/show?code=XZsP8PZbT7AJzG98tV1gnVoEsxKRbBl8awX

License

This dataset, including all its components, is released under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. To the extent possible under law, the authors have dedicated all copyright and related and neighboring rights to this dataset to the public domain worldwide. This dedication applies to the dataset and all derivative works.

The MatSeg 2D and 3D synthetic were generated using the open-images dataset which is licensed under the https://www.apache.org/licenses/LICENSE-2.0. For these components, you must comply with the terms of the Apache License. In addition, the MatSege3D dataset uses Shapenet 3D assets with GNU license.

Example Usage:

An Example of a training and evaluation code for a net trained on the dataset and evaluated on the benchmark is given at these urls: 1, 2

This include an evaluation script on the MatSeg benchmark.

Training script using the MatSeg dataset.

And weights of a trained model

Paper:

More detail on the work ca be found in the paper "Infusing Synthetic Data with Real-World Patterns for
Zero-Shot Material State Segmentation"

Croissant metadata and additional sources for downloading the dataset are available at 1,2