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pottery_classifier
samanthajmichael/pottery_classifier
pottery_classifier is a machine learning model from samanthajmichael. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for keras. The card lists the license as mit.
- Task: Multiclass Ceramic Pottery Classification - Architecture: Multilayer Perceptron (MLP) - Input Features: 95 archaeological ceramic features - Output Classes: 9 distinct pottery types
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At a glance
- Library
- keras
- License
- mit
- Access
- Public
- Created
- Feb 22, 2025
- Updated
- Feb 22, 2025
- SHA
- 65bac289
Model card
From the Hugging Face model README
Pottery Classification Model
Model Description
- Task: Multiclass Ceramic Pottery Classification
- Architecture: Multilayer Perceptron (MLP)
- Input Features: 95 archaeological ceramic features
- Output Classes: 9 distinct pottery types
Model Details
- Performance: 99.31% Test Accuracy
- Test Loss: 0.0376
- Training Epochs: 500
- Batch Size: 256
Dataset
Name: Ceramics: Temporal-Spatial Dataset Year: 1988 Source: Digital Archaeological Record (tDAR) Identifier:
- tDAR ID: 6039
- DOI: 10.6067/XCV8TD9WNB
Description: Archaeological ceramic dataset containing 95 features across 9 distinct pottery types, collected to analyze spatial and temporal characteristics of ceramic artifacts.
Features:
- Total features: 95 after encoding
- Feature selection process: Detailed in companion EDA Notebook
| Feature | Description |
|---|---|
| firing | Firing atmosphere |
| temper | Type of temper used |
| manipul | Surface manipulation |
| compact | Surface compaction |
| color | Paint colors used |
| pnttype | Paint type (organic, mineral, clay) |
| cover | Surface slips/coatings |
| ware | Manufacturing technique groups |
| form | Vessel shape/type |
| culcat | Cultural Category |
Classes: 9 pottery types
Training Methodology
- Optimizer: Adam
- Learning Rate: Initial 0.01 with exponential decay
- Regularization:
- He Weight Initialization
- L2 Regularization
- Early Stopping
Intended Use
- Archaeological ceramic type classification
- Research in archaeological artifact analysis
- Pottery provenance studies
Limitations
- Trained on a specific archaeological dataset
- Performance may vary with different ceramic collections
- Requires careful preprocessing of input features
Citation
If you use this model, please cite: samanthajmichael/2025
License
MIT
- Library
- keras
- License
- mit
- Created
- Feb 22, 2025
- Updated
- Feb 22, 2025