concrete_patch_classification 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 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: A reorganized version of the original 111 patches with 5-fold splits. An extended set of 426 expert-annotated patches with an additional geometric defect class(Crushed in English, Écrasé in French). A collection of synthetic patches generated with StyleGAN3, covering all five classes. Sub-dataset 1: Original annotated texture windows Content: 111 labeled gray-leveled texture windows with fixed width 200 extracted from 24 raw images. 5-fold cross-validation Original classes: Fluid (24 images, proportion 21.62%) Good (27 images, proportion 24.32%) Dry (24 images, proportion 21.62%) Tearing (36 images, proportion 32.43%) Labels: texture_windows-labels.csv. Model weights fine-tuned in subdataset1 with synthetic images in subdataset3: efficientformerl3, inceptionresnetv2, vgg19, texture_model. (pth: model weight, *.txt: normalization params for image, *.npy: normalization params for texture descripteur vector) Sub-dataset 2: Extended expert-annotated texture windows Content: 426 extended labeled gray-leveled texture windows with fixed width 200 extracted from 24 raw images. 5-fold cross-validation Classes: Fluid(84 images,proportion 19.72%) Good(127 images,proportion 29.81%) Dry(68 images,proportion 15.96%) Tearing(61 images,proportion 14.32%) Geometric defect Écrasé (French) / Crushed (English) (86 images, proportion 20.19%) Labels: patch_labels(426extension).csv Model weights fine-tuned in subdataset2 with synthetic images in subdataset3: efficientformerl3, inceptionresnetv2, vgg19, texture_model, midfusion_model. (pth: model weight, *.txt: normalization params for image, *.npy: normalization params for texture descripteur vector) Sub-dataset 3: Synthetic texture windows (StyleGAN3 generated) Content: Synthetic gray-leveled texture windows generated by five separate pretrained generative models. Classes: Fluid(1200 images) Good(1200 images) Dry(1200 images) Tearing(1200 images) Geometric defect Écrasé (French) / Crushed (English)(1200 images) Labels: patch_labels(426extension+stylegan3).csv Model weights trained for generation: 4 category model weights trained by StyleGAN3 (fluid, good, dry, tearing), each model can only generate one category. StyleGAN3 trains network-snapshot-001501.pkl with 5 categories, and it is used to create the class "ecrase" For specific dataset usage, please refer to the GitHub repository License 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/ 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.