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--- |
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tags: |
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- adaptive-sparse-training |
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- energy-efficient |
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- sustainability |
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metrics: |
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- accuracy |
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- energy_savings |
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license: mit |
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language: |
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- en |
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--- |
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# resnet18 (AST-Trained) |
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**Trained with 65% less energy than standard training** ⚡ |
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## Model Details |
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- **Architecture:** resnet18 |
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- **Dataset:** CIFAR-10 |
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- **Training Method:** Adaptive Sparse Training (AST) |
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- **Target Activation Rate:** 35% |
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## Performance |
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- **Accuracy:** 6809.00% |
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- **Energy Savings:** 65% |
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- **Training Epochs:** 10 |
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## Sustainability Report |
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This model was trained using Adaptive Sparse Training, which dynamically selects |
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the most important training samples. This resulted in: |
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- ⚡ **65% energy savings** compared to standard training |
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- 🌍 **Lower carbon footprint** |
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- ⏱️ **Faster training time** |
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- 🎯 **Maintained accuracy** (minimal degradation) |
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## How to Use |
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```python |
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import torch |
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from torchvision import models |
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# Load model |
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model = models.resnet18(num_classes=10) |
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model.load_state_dict(torch.load("pytorch_model.bin")) |
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model.eval() |
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# Inference |
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# ... (your inference code) |
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``` |
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## Training Details |
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**AST Configuration:** |
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- Target Activation Rate: 35% |
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- Adaptive PI Controller: Enabled |
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- Mixed Precision (AMP): Enabled |
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## Reproducing This Model |
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```bash |
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pip install adaptive-sparse-training |
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python -c " |
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from adaptive_sparse_training import AdaptiveSparseTrainer, ASTConfig |
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config = ASTConfig(target_activation_rate=0.35) |
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# ... (full training code) |
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" |
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``` |
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## Citation |
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If you use this model or AST, please cite: |
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```bibtex |
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@software{adaptive_sparse_training, |
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title={Adaptive Sparse Training}, |
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author={Idiakhoa, Oluwafemi}, |
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year={2024}, |
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url={https://github.com/oluwafemidiakhoa/adaptive-sparse-training} |
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} |
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``` |
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## Acknowledgments |
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Trained using the `adaptive-sparse-training` package. Special thanks to the PyTorch and HuggingFace communities. |
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--- |
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*This model card was auto-generated by the AST Training Dashboard.* |
