Duplicate from Voxel51/OD_MetalDAM

Co-authored-by: Harpreet Sahota <harpreetsahota@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,59 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs 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

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. For production use, please validate models with domain experts.