| # 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 | |