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concrete_patch_classification

\n

This dataset is based on the original dataset I3DCP introduced in Rill-García, R., Dokladalova, E., Dokládal, P., Caron, J.-F., Mesnil, R., Margerit, P., & Charrier, M. (2022). Inline monitoring of 3D concrete printing using computer vision. Additive Manufacturing, 60, 103175. https://doi.org/10.1016/j.addma.2022.103175

\n

The original dataset includes raw images of cement-based material deposition, segmentation masks of interstitial lines, and texture classification patches. In particular, our work focuses on the texture classification patches.  This dataset thus provides three complementary resources:

\n
    \n
  1. A reorganized version of the original 111 patches with 5-fold splits.
    2. \n
  2. An extended set of 426 expert-annotated patches with an additional geometric defect class(Crushed in English, Écrasé in French).
    3. \n
  3. A collection of synthetic patches generated with StyleGAN3, covering all five classes.
    4. \n
\n

Sub-dataset 1: Original annotated texture windows

\n\n\n

Sub-dataset 2: Extended expert-annotated texture windows

\n\n

Sub-dataset 3: Synthetic images (StyleGAN3 generated)

\n\n

 

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For specific dataset usage, please refer to the GitHub repository

\n

 

\n

Updates (compared to Version 1.0.0)

\n

The models were re-trained under an updated training configuration, resulting in reduced overfitting compared to Version 1.0.0. In addition, the inference procedure has been upgraded from a single-model setup to a 5-model ensemble strategy based on logits averaging. 

\n

Synthetic Data Extension (SubDataset3)

\n

The synthetic image dataset has been expanded. In Version 1.0.0, synthetic images were generated exclusively using a generator trained on Original dataset. In the current version, additional synthetic images generated by a generator trained on Re-annotated dataset have been included. The corresponding label CSV files are also provided to facilitate data augmentation during training.

\n

To avoid data leakage between datasets, a cross-dataset generation strategy is adopted:

\n\n

 

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License

\n

This dataset is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).  https://creativecommons.org/licenses/by-nc-sa/4.0/

\n

It is derived from the I3DCP released under the same license (CC BY-NC-SA 4.0). Additional annotations and processing were created by us and are released under the same CC BY-NC-SA 4.0 license.

", "funding": [ { "award": { "number": "ANR-24-CE10-6002-01", "title": { "en": "ANR-JCJC SmartAMP" } }, "funder": { "id": "00rbzpz17", "name": "Agence Nationale de la Recherche" } } ], "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2026-06-10", "publisher": "Zenodo", "references": [ { "reference": "Rill-Garc\u00eda R, Dokladalova E, Dokl\u00e1dal P, et al. Inline monitoring of 3D concrete printing using computer vision[J]. Additive Manufacturing, 2022, 60: 103175. https://doi.org/10.1016/j.addma.2022.103175" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "" }, "icon": "cc-by-nc-sa-icon", "id": "cc-by-nc-sa-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution Non Commercial Share Alike 4.0 International" } } ], "subjects": [ { "subject": "3D concrete printing" }, { "subject": "Texture Classification" }, { "subject": "generative adversarial network" } ], "title": "[LS2N_ROMAS_IPI]Concrete patch classification", "version": "1.0.1" }, "parent": { "access": { "owned_by": { "user": "1404632" }, "settings": { "accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": true, "secret_link_expiration": 0 } }, "communities": { "default": "4aff5dc6-1683-46ea-a7df-9d95d91d4c65", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_submission_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2023-10-10T15:54:10.793638+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "4aff5dc6-1683-46ea-a7df-9d95d91d4c65", "links": {}, "metadata": { "curation_policy": "", "page": "", "title": "Image Perception Interaction team - LS2N lab" }, "revision_id": 0, "slug": "ls2n-ipi", "updated": "2023-10-10T15:54:10.793646+00:00" }, { "access": { "member_policy": "open", "members_visibility": "public", "record_submission_policy": "open", "review_policy": "closed", "visibility": "public" }, "children": { "allow": false }, "created": "2025-09-15T14:08:17.770800+00:00", "custom_fields": { "subjects": [ { "id": "euroscivoc:1247" }, { "id": "mesh:D012371" }, { "id": "gemet:concept/8337" } ] }, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "c166cea0-4517-46fb-9f6d-8238642c0220", "links": {}, "metadata": { "organizations": [ { "id": "03gnr7b55" }, { "id": "03nh7d505" } ], "title": "ROMAS_LS2N_UMR6004", "website": "https://www.ls2n.fr/equipe/romas/" }, "revision_id": 3, "slug": "romas_ls2n_umr6004", "updated": "2025-09-15T14:10:37.695699+00:00" } ], "ids": [ "4aff5dc6-1683-46ea-a7df-9d95d91d4c65", "c166cea0-4517-46fb-9f6d-8238642c0220" ] }, "id": "17078771", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.17078771", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.20628313", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:20628313", "provider": "oai" } }, "revision_id": 8, "stats": { "all_versions": { "data_volume": 781791741302.0, "downloads": 279, "unique_downloads": 193, "unique_views": 341, "views": 419 }, "this_version": { "data_volume": 271619777723.0, "downloads": 80, "unique_downloads": 70, "unique_views": 96, "views": 107 } }, "status": "published", "swh": {}, "updated": "2026-06-29T14:12:19.931983+00:00", "versions": { "index": 2, "is_latest": true } }