Defect Detection · foundry-ml
mask_rcnn_defect_detection_v1-1
Microscopy dataset annotated for instance-segmentation defect detection in materials inspection.
What mask_rcnn_defect_detection_v1-1 is
mask_rcnn_defect_detection_v1-1 is a materials science and engineering dataset curated by foundry-ml. It consists of microscopy images prepared for identifying, localising, and segmenting structural defects in materials.
The dataset is designed specifically to serve as training data for Mask R-CNN and related instance-segmentation models, supporting automated visual quality control and materials inspection workflows.
How it was made
The dataset comprises microscopy imagery accompanied by instance-segmentation defect annotations. These annotations delineate individual defect boundaries to facilitate supervised training and evaluation of Mask R-CNN models.
Key facts
- Publisher
- foundry-ml
- Fields
- Materials science, engineering
- Primary task
- Defect detection and instance segmentation
- Target architecture
- Mask R-CNN
- Input modality
- Microscopy images
What you can use it for
- Materials defect inspection
- Locating and delineating morphological and physical defects within materials microscopy imagery.
- Instance segmentation training
- Providing annotated ground truth to train Mask R-CNN networks for defect identification tasks.
- Automated quality assessment
- Evaluating automated visual inspection routines in materials engineering workflows.
Explore the data
No licence declared
The authors have not published a licence, which means no permission to redistribute the data has been granted to anyone. The rows are not served here; read them on the original dataset page.Strengths and limits
Good at
- Provides targeted instance-level annotations suited for complex defect geometries rather than simple bounding boxes.
- Designed directly for use with established Mask R-CNN architectures in materials science.
Watch out for
- The documentation does not provide details on sample counts, image resolutions, or the specific materials analysed.
- Designed specifically for microscopy-based defect inspection, limiting direct applicability to other imaging modalities.
Questions
Can I look inside mask_rcnn_defect_detection_v1-1 without downloading it?
Not here: it declares no licence, so no permission to redistribute its rows has been granted. Its description and statistics are still shown.
What licence is mask_rcnn_defect_detection_v1-1 under?
Its authors have not declared a licence, which means no permission to reuse or redistribute it has been granted.
What type of data does this dataset contain?
It contains microscopy images annotated for instance-segmentation defect detection.
Which models is this dataset structured to train?
It is structured specifically as training data for Mask R-CNN instance-segmentation models.
What research disciplines does this dataset address?
It addresses defect identification and quality control within materials science and engineering.
Written from mask_rcnn_defect_detection_v1-1's own dataset card. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.