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merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..06f8aa956b3a60c0818d77ff9023cc4c5207df1a --- /dev/null +++ b/README.md @@ -0,0 +1,419 @@ +--- +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. 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