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--- |
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language: |
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- en |
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tags: |
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- automl |
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- tabular-classification |
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- autogluon |
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- cmu-course |
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datasets: |
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- aedupuga/lego-sizes |
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metrics: |
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- type: accuracy |
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- type: f1 |
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model-index: |
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- name: Lego Brick Classification (Classical AutoML) |
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results: |
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- task: |
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type: tabular-classification |
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name: Tabular Classification |
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dataset: |
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name: aedupuga/lego-sizes |
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type: classification |
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split: augmented |
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metrics: |
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- type: accuracy |
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value: 0.97 |
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- type: f1 |
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value: 0.96 |
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- task: |
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type: tabular-classification |
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name: Tabular Classification |
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dataset: |
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name: aedupuga/lego-sizes |
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type: classification |
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split: original |
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metrics: |
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- type: accuracy |
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value: 0.90 |
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- type: f1 |
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value: 0.89 |
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--- |
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# Model Card for Lego Brick Classification (Classical AutoML) |
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This model classifies LEGO pieces into three types — **Standard**, **Flat**, and **Sloped** — using their geometric dimensions (*Length, Height, Width, Studs*). |
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It was trained using **AutoGluon Tabular AutoML**, which automatically searched over classical ML models (LightGBM, XGBoost, CatBoost, Random Forest, k-NN, Neural Network) and selected the best-performing one. |
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--- |
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## Model Details |
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### Model Description |
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- **Developed by:** Xinxuan Tang (CMU) |
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- **Dataset curated by:** Anuhya Edupuganti (CMU) |
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- **Model type:** AutoML ensemble (best model = LightGBM) |
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- **Language(s):** N/A (tabular data) |
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- **Finetuned from:** Not applicable |
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### Model Sources |
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- **Repository:** [Hugging Face Model Repo](https://huggingface.co/) |
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- **Dataset:** [aedupuga/lego-sizes](https://huggingface.co/datasets/aedupuga/lego-sizes) |
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--- |
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## Uses |
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### Direct Use |
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- Educational practice in **tabular classification**. |
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- Experimenting with AutoML search and hyperparameter tuning. |
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### Downstream Use |
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- Could be used as a **teaching example** for AutoML pipelines on small tabular datasets. |
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### Out-of-Scope Use |
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- **Not suitable for industrial LEGO quality control**, since dataset is synthetic and small. |
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--- |
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## Bias, Risks, and Limitations |
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- **Small dataset**: only 30 original bricks, augmented to 300 synthetic samples. |
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- **Synthetic data bias**: jitter augmentation may not reflect real-world LEGO variations. |
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### Recommendations |
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Users should treat results as **proof-of-concept** and not deploy in production. |
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--- |
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## How to Get Started with the Model |
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```python |
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from autogluon.tabular import TabularPredictor |
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import pandas as pd |
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# Load trained predictor |
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predictor = TabularPredictor.load("autogluon_model/") |
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# Run inference |
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test_data = pd.DataFrame([{"Length": 4, "Height": 1.2, "Width": 2, "Studs": 4}]) |
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print(predictor.predict(test_data)) |
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