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| annotations_creators: [] | |
| language: en | |
| size_categories: | |
| - n<1K | |
| task_categories: | |
| - image-segmentation | |
| task_ids: [] | |
| pretty_name: OD_MetalDAM | |
| tags: | |
| - fiftyone | |
| - image | |
| - image-segmentation | |
| dataset_summary: > | |
| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 42 | |
| samples. | |
| ## Installation | |
| If you haven't already, install FiftyOne: | |
| ```bash | |
| pip install -U fiftyone | |
| ``` | |
| ## Usage | |
| ```python | |
| import fiftyone as fo | |
| from fiftyone.utils.huggingface import load_from_hub | |
| # Load the dataset | |
| # Note: other available arguments include 'max_samples', etc | |
| dataset = load_from_hub("Voxel51/OD_MetalDAM") | |
| # Launch the App | |
| session = fo.launch_app(dataset) | |
| ``` | |
| license: mit | |
| # Dataset Card for OD_MetalDAM | |
| The OD_MetalDAM (Metallography Dataset from Additive Manufacturing) is a specialized computer vision dataset containing 42 high-resolution scanning electron microscope (SEM) images of metal microstructures from additive manufacturing processes. | |
| Each image includes pixel-level semantic segmentation masks identifying five distinct metallurgical phases and features: Matrix, Austenite, Martensite/Austenite, Precipitate, and Defects. | |
| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 42 samples. | |
| ## Installation | |
| If you haven't already, install FiftyOne: | |
| ```bash | |
| pip install -U fiftyone | |
| ``` | |
| ## Usage | |
| ```python | |
| import fiftyone as fo | |
| from fiftyone.utils.huggingface import load_from_hub | |
| # Load the dataset | |
| # Note: other available arguments include 'max_samples', etc | |
| dataset = load_from_hub("Voxel51/OD_MetalDAM") | |
| # Launch the App | |
| session = fo.launch_app(dataset) | |
| ``` | |
| ## Dataset Details | |
| ### Dataset Description | |
| The OD_MetalDAM dataset is a comprehensive metallography dataset specifically designed for computer vision applications in materials science and additive manufacturing quality control. | |
| The dataset consists of 42 carefully curated scanning electron microscope (SEM) images of metal microstructures, each accompanied by detailed pixel-level segmentation masks and rich metadata. | |
| Key features of the dataset: | |
| - **High-resolution SEM images**: Each micrograph is captured at various magnifications (5,000x to 15,000x) | |
| - **Pixel-level annotations**: Five distinct classes representing different metallurgical phases and features | |
| - **Comprehensive metadata**: Including magnification levels, scale bar measurements, and pixel counts for each phase | |
| - **Pre-processed images**: Images are cropped to remove information bands, ensuring clean training data | |
| - **Semantic segmentation masks**: Color-coded masks for easy visualization and model training | |
| The dataset addresses the critical need for automated microstructure analysis in additive manufacturing, where understanding phase distributions, defect detection, and material characterization are essential for quality assurance and process optimization. | |
| - **Curated by:** ArcelorMittal and DaSCI (Andalusian Research Institute in Data Science and Computational Intelligence) | |
| - **Funded by:** ArcelorMittal | |
| - **Shared by:** Harpreet Sahota | |
| - **Language(s) (NLP):** en | |
| - **Dataset License:** MIT License | |
| ### Dataset Sources | |
| - **Repository:** https://github.com/ari-dasci/OD-MetalDAM | |
| ## Uses | |
| ### Direct Use | |
| The OD_MetalDAM dataset is intended for: | |
| 1. **Semantic Segmentation Model Training**: Train deep learning models to automatically segment and classify different metallurgical phases in SEM images | |
| 2. **Quality Control in Additive Manufacturing**: Develop automated inspection systems for detecting defects and analyzing microstructure quality | |
| 3. **Materials Science Research**: Study phase distributions, grain boundaries, and microstructural features in metal alloys | |
| 4. **Computer Vision Algorithm Development**: Benchmark and evaluate segmentation algorithms on high-resolution microscopy data | |
| 5. **Educational Purposes**: Teach materials characterization and computer vision techniques in metallurgy | |
| 6. **Transfer Learning**: Pre-train models for other microscopy or materials science applications | |
| ### Out-of-Scope Use | |
| This dataset should NOT be used for: | |
| 1. **Medical diagnosis or healthcare applications**: The dataset is specific to metal microstructures and not suitable for biological or medical imaging | |
| 2. **Real-time production monitoring**: With only 42 samples, the dataset may not capture all possible variations in production environments | |
| 3. **Other material types**: The dataset is specific to metal alloys from additive manufacturing and may not generalize to ceramics, polymers, or composites | |
| 4. **Macro-scale defect detection**: The dataset focuses on microstructural features and is not suitable for detecting large-scale manufacturing defects | |
| 5. **Standalone production decisions**: Models trained on this dataset should be validated with domain experts before deployment in critical manufacturing processes | |
| ## Dataset Structure | |
| Each sample in the dataset contains: | |
| ### Image Data | |
| - **filepath**: Path to the cropped SEM micrograph (JPEG format) | |
| - **mask**: Semantic segmentation mask (PNG format) with pixel values corresponding to class labels | |
| ### Segmentation Classes | |
| - **0 - Matrix**: Background matrix material (base metal structure) | |
| - **1 - Austenite**: Austenite phase regions | |
| - **2 - Martensite/Austenite**: Mixed or transitional phase regions | |
| - **3 - Precipitate**: Precipitate particles and inclusions | |
| - **4 - Defect**: Defects, voids, and artifacts | |
| ### Metadata Fields | |
| - **micron_bar**: Scale bar value in micrometers (μm) | |
| - **magnification**: SEM magnification level (5000x, 10000x, or 15000x) | |
| - **label0_pixels**: Pixel count for Matrix phase | |
| - **label1_pixels**: Pixel count for Austenite phase | |
| - **label2_pixels**: Pixel count for Martensite/Austenite phase | |
| - **label3_pixels**: Pixel count for Precipitate phase | |
| - **label4_pixels**: Pixel count for Defect phase | |
| - **total_pixels**: Total image pixels after cropping | |
| ### Image Specifications | |
| - **Resolution**: Varies between 1024×703 and 1280×895 pixels (after cropping) | |
| - **Format**: JPEG for images, PNG for segmentation masks | |
| - **Color**: Grayscale SEM images, color-coded segmentation masks | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| The OD_MetalDAM dataset was created to address several critical challenges in additive manufacturing and materials science: | |
| 1. **Automation Need**: Manual analysis of microstructures is time-consuming and subject to human error | |
| 2. **Quality Assurance**: Additive manufacturing processes require rigorous quality control to ensure material properties | |
| 3. **Standardization**: Provide a benchmark dataset for developing and comparing computer vision algorithms in metallography | |
| 4. **Research Advancement**: Enable machine learning research in materials characterization | |
| 5. **Industrial Application**: Bridge the gap between academic research and industrial quality control needs | |
| ### Source Data | |
| #### Data Collection and Processing | |
| The data collection and processing pipeline involved: | |
| 1. **Sample Preparation**: Metal samples from additive manufacturing processes were prepared using standard metallographic techniques | |
| 2. **SEM Imaging**: High-resolution images captured using scanning electron microscopy at multiple magnifications | |
| 3. **Expert Annotation**: Metallurgists and materials scientists manually annotated each image to identify different phases | |
| 4. **Data Preprocessing**: | |
| - Original images contained information bands that were cropped out | |
| - Segmentation masks were generated with consistent color coding | |
| - Metadata was extracted and stored in SQL database format | |
| 5. **Quality Control**: Each annotation was reviewed for accuracy and consistency | |
| 6. **Format Conversion**: Data converted to FiftyOne-compatible format for easy access and visualization | |
| #### Who are the source data producers? | |
| The source data was produced by: | |
| - **ArcelorMittal**: Global steel manufacturing company providing metal samples and domain expertise | |
| - **DaSCI (Andalusian Research Institute)**: Research institute providing computer vision and data science expertise | |
| - **Materials Scientists**: Expert metallurgists who performed the microscopy and initial analysis | |
| - **Research Engineers**: Technical staff who prepared samples and operated SEM equipment | |
| ### Annotations | |
| #### Annotation process | |
| The annotation process followed these steps: | |
| 1. **Initial Segmentation**: Expert metallurgists manually segmented each SEM image using specialized image analysis software | |
| 2. **Phase Identification**: Each region was classified into one of five categories based on visual characteristics and domain knowledge | |
| 3. **Pixel-level Precision**: Annotations were performed at pixel level to capture fine grain boundaries and small features | |
| 4. **Validation**: Multiple experts reviewed annotations for consistency | |
| 5. **Color Coding**: Segmentation masks were generated with consistent color mapping for visualization | |
| #### Who are the annotators? | |
| Annotations were created by: | |
| - Professional metallurgists with expertise in additive manufacturing | |
| - Materials science researchers from DaSCI and ArcelorMittal | |
| - Domain experts with specific knowledge of steel microstructures and phase identification | |
| #### Personal and Sensitive Information | |
| The dataset does not contain any personal, sensitive, or private information. All data consists of: | |
| - Technical microscopy images of metal samples | |
| - Scientific measurements and metadata | |
| - No human subjects or personal identifiers | |
| - No location-specific or proprietary process information | |
| ## Bias, Risks, and Limitations | |
| ### Technical Limitations | |
| 1. **Limited Sample Size**: With 42 samples, the dataset may not capture all possible microstructural variations | |
| 2. **Specific Material System**: Dataset focuses on specific steel alloys used in additive manufacturing | |
| 3. **Magnification Range**: Limited to 5,000x-15,000x magnification, may not capture nano-scale or macro-scale features | |
| 4. **2D Representation**: SEM images provide 2D views of 3D microstructures | |
| ### Potential Biases | |
| 1. **Manufacturing Process**: Samples may be biased toward specific additive manufacturing techniques | |
| 2. **Material Composition**: Limited to certain alloy compositions used by ArcelorMittal | |
| 3. **Quality Distribution**: May not represent the full spectrum of quality variations in production | |
| ### Risks | |
| 1. **Overfitting**: Small dataset size increases risk of model overfitting | |
| 2. **Domain Shift**: Models may not generalize to different alloys, manufacturing processes, or imaging conditions | |
| 3. **Annotation Subjectivity**: Some phase boundaries may be ambiguous and subject to expert interpretation | |
| ### Recommendations | |
| Users should be made aware of the risks, biases and limitations of the dataset. Recommendations include: | |
| 1. **Data Augmentation**: Apply appropriate augmentation techniques to increase training data diversity | |
| 2. **Transfer Learning**: Use pre-trained models and fine-tune on this dataset for better performance | |
| 3. **Domain Validation**: Validate models on independent datasets from your specific application domain | |
| 4. **Expert Review**: Have domain experts review model predictions before deployment | |
| 5. **Ensemble Methods**: Combine multiple models to improve robustness | |
| 6. **Continuous Learning**: Update models as more data becomes available from production environments | |
| 7. **Cross-validation**: Use appropriate cross-validation strategies given the limited sample size | |
| ## Glossary | |
| - **SEM**: Scanning Electron Microscopy - High-resolution imaging technique for surface analysis | |
| - **Austenite**: Face-centered cubic crystal structure phase of steel | |
| - **Martensite**: Body-centered tetragonal crystal structure formed by rapid cooling | |
| - **Precipitate**: Secondary phase particles that form within the metal matrix | |
| - **Matrix**: Primary continuous phase in the microstructure | |
| - **Additive Manufacturing**: 3D printing process for metals using layer-by-layer deposition | |
| - **Microstructure**: Microscopic structure of a material revealing grains, phases, and defects | |
| - **Magnification**: Degree of enlargement of the microscope image | |
| - **Micron Bar**: Scale reference showing actual size in micrometers (μm) | |
| ## More Information | |
| For additional information about the dataset: | |
| - **GitHub Repository**: https://github.com/ari-dasci/OD-MetalDAM | |
| - **FiftyOne Documentation**: https://docs.voxel51.com/ | |
| - **Materials Science Background**: Consult metallography textbooks and additive manufacturing literature | |
| - **Technical Support**: Open issues on the GitHub repository | |
| ## Dataset Card Authors | |
| - Harpreet Sahota - Dataset conversion to FiftyOne format and Hugging Face integration | |
| - Original dataset creators from ArcelorMittal and DaSCI | |
| ## 📖 Citation | |
| If you use this dataset in your research, please cite: | |
| **BibTeX:** | |
| ```bibtex | |
| @misc{metaldam2024, | |
| title={MetalDAM: Metallography Dataset from Additive Manufacturing}, | |
| author={{ArcelorMittal} and {DaSCI Andalusian Research Institute}}, | |
| year={2024}, | |
| url={https://github.com/ari-dasci/OD-MetalDAM}, | |
| note={FiftyOne dataset available at: https://huggingface.co/datasets/Voxel51/OD_MetalDAM} | |
| } | |
| ``` | |
| **APA:** | |
| ArcelorMittal & DaSCI Andalusian Research Institute. (2024). *MetalDAM: Metallography Dataset from Additive Manufacturing* [Data set]. GitHub. https://github.com/ari-dasci/OD-MetalDAM | |
| ## 📄 License | |
| The dataset is licensed under the MIT License, but the code is licensed under the Apache-2.0 License - see the [LICENSE](LICENSE) file for details. | |
| ## 👥 Authors | |
| - **Original Dataset**: ArcelorMittal & DaSCI Andalusian Research Institute | |
| - **FiftyOne Integration**: Harpreet Sahota | |
| ## 🙏 Acknowledgments | |
| - ArcelorMittal for providing the metallography images and domain expertise | |
| - DaSCI Andalusian Research Institute for dataset curation and annotation | |
| - FiftyOne team for the excellent visualization framework | |
| --- | |
| **Note**: This dataset is intended for research and educational purposes in materials science and computer vision. For production use, please validate models with domain experts. |