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--- |
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license: mit |
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datasets: |
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- ArtifactClfDurham/OrientalMuseum-white |
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language: |
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- en |
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base_model: |
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- google/efficientnet-b0 |
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tags: |
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- artifact |
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- museum |
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--- |
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# Artifact Classification Model v2 - Best Model Usage Guide |
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This directory contains the improved v2 artifact classification model with state-of-the-art performance for classifying museum artifacts by both object type and material. |
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## Hosted Model |
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The best model is available on Hugging Face at: **[SpyC0der77/artifact-efficientnet](https://huggingface.co/SpyC0der77/artifact-efficientnet)** |
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You can use the model directly from Hugging Face without downloading it locally. |
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## Model Overview |
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The v2 model is an advanced multi-output neural network that predicts two attributes simultaneously: |
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- **Object Name**: The type/category of the artifact (e.g., "vase", "statue", "pottery") |
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- **Material**: The material composition (e.g., "ceramic", "bronze", "stone") |
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### Key Improvements Over v1 |
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- **EfficientNet Backbone**: Uses EfficientNet-B0 instead of ResNet-50 for better feature extraction |
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- **Attention Mechanism**: Includes an attention layer to focus on relevant features |
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- **Advanced Training**: Incorporates CutMix augmentation, Focal Loss, and mixed precision training |
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- **Better Regularization**: Uses dropout and batch normalization for improved generalization |
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## Architecture & Usage |
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The v2 model uses an EfficientNet-B0 backbone with an attention mechanism for multi-output classification. It processes RGB images of artifacts and outputs predictions for both object type and material composition. |
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### Input |
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- **Format**: RGB images (224×224 pixels after preprocessing) |
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- **Preprocessing**: Resize to 256×256, center crop to 224×224, normalize with ImageNet statistics |
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### Output |
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- **Object Classification**: Predicts artifact type (e.g., "vase", "statue", "pottery") |
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- **Material Classification**: Predicts material composition (e.g., "ceramic", "bronze", "stone") |
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- **Confidence Scores**: Probability scores for each prediction |
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- **Format**: Dictionary with 'object_name' and 'material' logits |
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## Model Architecture |
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```python |
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ImprovedMultiOutputModel( |
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backbone: EfficientNet-B0 (pretrained) |
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attention: Linear(1280 → 512 → 1280) with Sigmoid |
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object_classifier: Linear(1280 → 1024 → 512 → num_object_classes) |
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material_classifier: Linear(1280 → 1024 → 512 → num_material_classes) |
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) |
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``` |
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### Input Requirements |
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- **Image Size**: 224×224 pixels (automatically resized and cropped) |
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- **Format**: RGB images |
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- **Normalization**: ImageNet normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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### Output Format |
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Returns a dictionary with: |
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- `'object_name'`: Logits for object classification |
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- `'material'`: Logits for material classification |
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## Training Details |
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The model was trained with the following configuration: |
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- **Dataset**: ArtifactClfDurham/OrientalMuseum-white |
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- **Training Split**: 85% of data |
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- **Validation Split**: 15% of data |
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- **Batch Size**: 32 |
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- **Epochs**: 20 |
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- **Optimizer**: AdamW with differential learning rates |
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- Backbone: 2e-4 (0.1× base LR) |
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- Heads: 2e-3 (base LR) |
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- **Augmentation**: Advanced (CutMix, rotation, color jitter, Gaussian blur) |
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- **Loss Function**: Cross-Entropy (or Focal Loss if enabled) |
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- **Scheduler**: Cosine annealing with warmup |
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### Advanced Training Features |
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- **CutMix Augmentation**: Randomly mixes image patches between samples |
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- **Focal Loss**: Addresses class imbalance (optional) |
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- **Mixed Precision**: Automatic mixed precision training for speed |
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- **Gradient Scaling**: Prevents gradient underflow |
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- **Early Stopping**: Saves best model based on validation accuracy |
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## Troubleshooting |
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### Common Issues |
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1. **CUDA Out of Memory** |
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- Reduce batch size: `--batch_size 8` |
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- Use CPU: Set device to "cpu" |
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2. **Import Errors** |
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- Ensure all dependencies are installed |
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- Check Python path includes project root |
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3. **Model Loading Errors** |
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- Verify the model file path is correct |
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- Ensure PyTorch version compatibility |
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4. **Low Confidence Scores** |
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- Model may not be trained on similar artifacts |
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- Check image preprocessing matches training setup |
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### Performance Tips |
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- Use GPU for faster inference |
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- Process images in batches for efficiency |
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- Consider model quantization for deployment |
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## Model Limitations |
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- Trained specifically on Oriental Museum artifacts |
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- May not generalize well to artifacts from other cultures/regions |
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- Performance depends on image quality and lighting |
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- Multi-output nature may have trade-offs between object and material accuracy |
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