---
tags:
- adaptive-sparse-training
- energy-efficient
- sustainability
metrics:
- accuracy
- energy_savings
license: mit
language:
- en
---

# resnet18 (AST-Trained)

**Trained with 65% less energy than standard training** ⚡

## Model Details
- **Architecture:** resnet18
- **Dataset:** CIFAR-10
- **Training Method:** Adaptive Sparse Training (AST)
- **Target Activation Rate:** 35%

## Performance
- **Accuracy:** 6809.00%
- **Energy Savings:** 65%
- **Training Epochs:** 10

## Sustainability Report
This model was trained using Adaptive Sparse Training, which dynamically selects
the most important training samples. This resulted in:

- ⚡ **65% energy savings** compared to standard training
- 🌍 **Lower carbon footprint**
- ⏱️ **Faster training time**
- 🎯 **Maintained accuracy** (minimal degradation)

## How to Use

```python
import torch
from torchvision import models

# Load model
model = models.resnet18(num_classes=10)
model.load_state_dict(torch.load("pytorch_model.bin"))
model.eval()

# Inference
# ... (your inference code)
```

## Training Details

**AST Configuration:**
- Target Activation Rate: 35%
- Adaptive PI Controller: Enabled
- Mixed Precision (AMP): Enabled

## Reproducing This Model

```bash
pip install adaptive-sparse-training

python -c "
from adaptive_sparse_training import AdaptiveSparseTrainer, ASTConfig
config = ASTConfig(target_activation_rate=0.35)
# ... (full training code)
"
```

## Citation

If you use this model or AST, please cite:

```bibtex
@software{adaptive_sparse_training,
    title={Adaptive Sparse Training},
    author={Idiakhoa, Oluwafemi},
    year={2024},
    url={https://github.com/oluwafemidiakhoa/adaptive-sparse-training}
}
```

## Acknowledgments

Trained using the `adaptive-sparse-training` package. Special thanks to the PyTorch and HuggingFace communities.

---

*This model card was auto-generated by the AST Training Dashboard.*