Add dummy discriminator model

README.md

@@ -0,0 +1,50 @@
+# Dummy Discriminator Model
+
+This is a dummy discriminator model for testing purposes.
+
+## 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

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c50b4f0c91bf95080a4cd18ca5205981c01d3769988e3d6db82aaaa0d59d8db0
+size 22274

model_metadata.json

@@ -0,0 +1,53 @@
+{
+  "model_id": "dummy_discriminator_v1",
+  "model_type": "detection",
+  "description": "Dummy discriminator model for testing",
+  "version": "1.0.0",
+  "author": "kenjon",
+  "architecture": {
+    "base_model": "custom_cnn",
+    "num_classes": 3,
+    "input_shape": [
+      3,
+      224,
+      224
+    ],
+    "output_shape": [
+      3
+    ],
+    "model_type": "detection"
+  },
+  "preprocessing": {
+    "normalization": "imagenet",
+    "resize": [
+      224,
+      224
+    ],
+    "augmentation": [
+      "random_horizontal_flip"
+    ]
+  },
+  "training": {
+    "optimizer": "adam",
+    "learning_rate": 0.001,
+    "batch_size": 32,
+    "epochs": 10,
+    "loss_function": "cross_entropy"
+  },
+  "performance": {
+    "accuracy": 0.85,
+    "precision": 0.83,
+    "recall": 0.87,
+    "f1_score": 0.85
+  },
+  "dependencies": {
+    "onnxruntime": ">=1.15.0",
+    "numpy": ">=1.21.0",
+    "torch": ">=2.0.0"
+  },
+  "usage": {
+    "input_format": "numpy array (3, 224, 224)",
+    "output_format": "numpy array (3,) - probabilities for [real, synthetic, semisynthetic]",
+    "example": "model.predict(image_array)"
+  }
+}

requirements.txt

@@ -0,0 +1,3 @@
+onnxruntime>=1.15.0
+numpy>=1.21.0
+torch>=2.0.0