# Dummy Discriminator Model

This is a dummy discriminator model for testing purposes, submitted by a BitMind subnet miner.

## Miner Information

- **UID**: 1
- **Coldkey**: 5Cvk3JRphVXXrwtJXP3xnDz9UF371P8ndAKfFA4JDxmTucQV
- **Hotkey**: 5FsPe1tZym7PgP9NqzEsiSG2bvuGCR9fPDBBFqUY1Hm56gwe
- **Network**: test
- **Subnet**: BitMind (netuid: 379)

## Model Information

- **Model Type**: Detection
- **Input**: RGB images (224x224)
- **Output**: 3-class classification (real, synthetic, semisynthetic)
- **Framework**: ONNX

## Usage

```python
import onnxruntime as ort
import numpy as np

# Load model
session = ort.InferenceSession("model.onnx")

# Prepare input
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)

# Run inference
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
outputs = session.run([output_name], {input_name: input_data})

# Get prediction
prediction = np.argmax(outputs[0][0])
classes = ["real", "synthetic", "semisynthetic"]
print(f"Prediction: {classes[prediction]}")
```

## Model Performance

- Accuracy: 85%
- Precision: 83%
- Recall: 87%
- F1-Score: 85%

## Dependencies

- onnxruntime >= 1.15.0
- numpy >= 1.21.0
- torch >= 2.0.0

## License

MIT License