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--- |
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license: mit |
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metrics: |
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- accuracy |
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library_name: keras |
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--- |
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# Pottery Classification Model |
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## Model Description |
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- **Task**: Multiclass Ceramic Pottery Classification |
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- **Architecture**: Multilayer Perceptron (MLP) |
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- **Input Features**: 95 archaeological ceramic features |
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- **Output Classes**: 9 distinct pottery types |
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## Model Details |
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- **Performance**: 99.31% Test Accuracy |
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- **Test Loss**: 0.0376 |
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- **Training Epochs**: 500 |
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- **Batch Size**: 256 |
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## Dataset |
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**Name**: Ceramics: Temporal-Spatial Dataset |
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**Year**: 1988 |
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**Source**: Digital Archaeological Record (tDAR) |
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**Identifier**: |
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- tDAR ID: 6039 |
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- DOI: 10.6067/XCV8TD9WNB |
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**Description**: |
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Archaeological ceramic dataset containing 95 features across 9 distinct pottery types, collected to analyze spatial and temporal characteristics of ceramic artifacts. |
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**Features**: |
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- Total features: 95 after encoding |
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- Feature selection process: Detailed in companion [EDA Notebook](https://github.com/samanthajmichael/deep_learning/blob/main/deep_learning/Assignments/pottery_classifier/notebooks/01_EDA.ipynb) |
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| Feature | Description | |
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|---------|-------------| |
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| firing | Firing atmosphere | |
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| temper | Type of temper used | |
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| manipul | Surface manipulation | |
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| compact | Surface compaction | |
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| color | Paint colors used | |
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| pnttype | Paint type (organic, mineral, clay) | |
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| cover | Surface slips/coatings | |
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| ware | Manufacturing technique groups | |
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| form | Vessel shape/type | |
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|culcat|Cultural Category| |
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**Classes**: 9 pottery types |
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## Training Methodology |
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- **Optimizer**: Adam |
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- **Learning Rate**: Initial 0.01 with exponential decay |
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- **Regularization**: |
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- He Weight Initialization |
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- L2 Regularization |
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- Early Stopping |
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## Intended Use |
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- Archaeological ceramic type classification |
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- Research in archaeological artifact analysis |
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- Pottery provenance studies |
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## Limitations |
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- Trained on a specific archaeological dataset |
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- Performance may vary with different ceramic collections |
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- Requires careful preprocessing of input features |
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## Citation |
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If you use this model, please cite: |
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samanthajmichael/2025 |
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## License |
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MIT |
