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README.md
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# Model Card for Lego Brick Classification (Classical AutoML)
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## Model Details
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- **Model type**: Classical AutoML ensemble (AutoGluon Tabular)
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- **Framework**: [AutoGluon](https://auto.gluon.ai/stable/index.html)
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- **Task**: Multiclass Classification
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- **Classes**:
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- Standard
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- Flat
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- Sloped
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The model was trained using AutoGluon Tabular with automated feature engineering, model selection, and hyperparameter tuning under a fixed budget.
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## Intended Use
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- **Direct use**: Classify LEGO bricks into `Standard`, `Flat`, or `Sloped` types based on their dimensions.
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- **Educational use**: Demonstrate classical AutoML techniques on a small synthetic dataset.
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- **Not intended for**: Industrial-scale LEGO piece classification or manufacturing quality control.
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## Dataset
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- **Dataset name**: [aedupuga/lego-sizes](https://huggingface.co/datasets/aedupuga/lego-sizes)
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- **Description**: Contains dimensions and stud count of LEGO bricks, with labels indicating piece type.
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- **Splits**:
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- Original: 30 measured samples
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- Augmented: 300 synthetically generated samples
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- **Features**:
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- Max Length (cm)
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- Max Height (cm)
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- Width (cm)
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- Studs
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- Type (Standard/Flat/Sloped)
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## Training Procedure
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- **Train/Val/Test split**: 80/10/10 (stratified) on augmented dataset
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- **External validation**: Performed on original dataset
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- **AutoML settings**:
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- Preset: `best_quality`
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- Metric: Accuracy
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- Time budget: ~30 minutes
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## Evaluation Results
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- **Augmented Test Set**:
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- Accuracy: 0.97
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- Weighted F1: 0.96
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- **Original External Validation Set**:
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- Accuracy: 0.93
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- Weighted F1: 0.92
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## Limitations
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- The dataset is very small (only 30 original samples).
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- Synthetic augmentation may not capture full real-world variability.
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- Model may not generalize beyond this toy dataset.
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## Recommendations
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- Use primarily for **demonstration and coursework**.
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- Do not deploy in real-world LEGO production environments.
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## Model Card Contact
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Author: *Xinxuan Tang (Carnegie Mellon University)* – xinxuant@andrew.cmu.edu
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Dataset curator: Anuhya Edupuganti (CMU) – aedupuga@andrew.cmu.edu
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