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