Optical microscopy images of cast iron alloys defects
The dataset comprises two subdatasets, featuring images of carbon defects based on cast iron samples adhering to European standards. The images are in .png format and are divided into training and test sets, with an additional split into 5 folds for reproducibility.The first sub-dataset contains ima...
| Autores: | , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/363741 |
| Acceso en línea: | http://hdl.handle.net/10261/363741 https://doi.org/10.20350/digitalCSIC/16484 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning Image classification Iron casting Microscopic imaging |
| Sumario: | The dataset comprises two subdatasets, featuring images of carbon defects based on cast iron samples adhering to European standards. The images are in .png format and are divided into training and test sets, with an additional split into 5 folds for reproducibility.The first sub-dataset contains images with only one type of defect per image. The second sub-dataset combines defects from consecutive categories to more closely represent real-world scenarios. Additionally, the second sub-dataset includes the corresponding semantic segmentation masks (MASK) as well as pre-trained models (.h5) for classification purposes. |
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