kenjon/dtest

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

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