Create README.md

README.md

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+---
+license: mit
+task_categories:
+- image-segmentation
+tags:
+- wood
+- defect-detection
+- quality-control
+- manufacturing
+- computer-vision
+- semantic-segmentation
+size_categories:
+- 1K<n<10K
+---
+
+# Dataset Card for Oak Defect Detection
+
+## Dataset Description
+
+### Dataset Summary
+
+The Oak Defect Detection dataset contains high-resolution line-scan imagery of green rough oak planks with pixel-level annotations for defect detection. The dataset is designed for semantic segmentation tasks to identify and classify defects in wood planks, including BlackRot, Knots, and Stains.
+
+This dataset was collected using line-scan imaging systems and includes both color images and corresponding binary masks for each defect class. The data has been processed and validated to ensure image-mask alignment and quality.
+
+### Supported Tasks and Leaderboards
+
+- **Semantic Segmentation**: The primary task is pixel-level classification of defects in oak planks
+- **Quality Control**: Can be used for automated quality assessment in wood processing
+- **Defect Classification**: Multi-class segmentation with classes: Background, BlackRot, Knot, Stain
+
+### Languages
+
+N/A (Image dataset)
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance consists of:
+- **Color Image**: High-resolution TIFF image (`*_Col.tif`) captured from line-scan system
+- **Binary Masks**: Per-class binary masks (`*_Col_Bin_<ClassName>.tif`) for each defect type
+- **Metadata**: Timestamp and position information encoded in filenames
+
+### Data Fields
+
+- **Images**: RGB TIFF format, variable resolution (typical line-scan dimensions)
+- **Masks**: Binary TIFF format, same resolution as corresponding images
+- **Class Labels**: 
+  - Background (class 0)
+  - BlackRot (class 1)
+  - Knot (class 2)
+  - Stain (class 3)
+
+### Data Splits
+
+The dataset includes train/validation/test splits stored in `split.json`. The splits are designed to ensure:
+- Representative distribution of defect classes
+- No data leakage between splits
+- Sufficient samples for training and evaluation
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created to support automated defect detection in wood processing pipelines. The line-scan imagery captures high-resolution details necessary for identifying subtle defects that affect wood quality and value.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+- **Data Collection**: Images captured using line-scan imaging systems
+- **Annotation**: Binary masks created for each defect class
+- **Normalization**: Defect class names were normalized (e.g., "Black_Rot" → "BlackRot") to ensure consistency
+- **Filtering**: Rare classes below a threshold were filtered to reduce class imbalance
+
+#### Who are the source language producers?
+
+Industrial line-scan imaging systems in wood processing facilities.
+
+### Annotations
+
+#### Annotation process
+
+Binary masks were created for each defect class, with each mask file corresponding to a specific defect type in a specific image.
+
+#### Who are the annotators?
+
+Annotations were created as part of the wood processing quality control workflow.
+
+### Personal and Sensitive Information
+
+No personal or sensitive information is present in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset supports automated quality control in wood processing, which can:
+- Improve efficiency in manufacturing
+- Reduce waste through early defect detection
+- Standardize quality assessment processes
+
+### Discussion of Biases
+
+- **Class Imbalance**: Some defect classes (e.g., Knot) may be more common than others (e.g., Stain)
+- **Image Conditions**: All images captured under controlled line-scan conditions
+- **Geographic Bias**: Dataset may reflect specific wood sources or processing conditions
+
+### Other Known Limitations
+
+- Limited to green rough oak planks
+- Line-scan specific imaging conditions
+- Some rare defect classes may have been filtered out during preprocessing
+
+## Additional Information
+
+### Dataset Curators
+
+Created by nrodgers98 for oak defect detection research and applications.
+
+### Licensing Information
+
+MIT License