Update README.md

README.md

@@ -59,14 +59,66 @@ This model is designed for **Edge AI deployment**, optimized via **ONNX** and **
 
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-## Training Configuration
-| Parameter | Value |
-|------------|--------|
-| Batch Size | 32 |
-| Epochs | 50 |
-| Optimizer | Adam |
-| Learning Rate | 1e-3 |
-| Loss Function | MSELoss |
+## Key Configuration Parameters
+
+- **Image Size**: 304×304 pixels
+- **Batch Size**: 32
+- **Learning Rate**: 1e-3
+- **Epochs**: 10
+- **Loss Function**: MSE Loss
+- **Optimizer**: Adam
+
+
+## Model Outputs
+
+The training script generates:
+
+- `casting_autoencoder.pth` - PyTorch model weights
+- `casting_autoencoder.onnx` - ONNX export for deployment
+- Calibrated anomaly threshold based on defective samples
+
+
+## Anomaly Detection Process
+
+1. **Training Phase**: Model learns to reconstruct normal casting images
+2. **Threshold Calibration**: Uses defective samples to determine optimal threshold
+3. **Inference**: Images with reconstruction error > threshold are flagged as defective
+
+## Performance
+
+- **Final Training Loss**: 0.0005
+- **Suggested Threshold**: 0.0004
+- **Model Type**: Unsupervised anomaly detection
+- **Architecture**: Convolutional Autoencoder
+
+
+## Applications
+
+This model is designed for:
+
+- Quality control in metal casting manufacturing
+- Real-time defect detection on production lines
+- Automated visual inspection systems
+- Edge deployment in industrial environments
+
+
+## Model Features
+
+- **Unsupervised Learning**: Trained only on normal samples
+- **Real-time Capable**: Optimized for edge deployment
+- **ONNX Compatible**: Ready for production deployment
+- **Automatic Thresholding**: Self-calibrating anomaly detection
+- **Industrial Grade**: Tested on real manufacturing data
+
+
+## Technical Details
+
+The model uses a symmetric encoder-decoder architecture with:
+
+- Stride-2 convolutions for downsampling
+- Transposed convolutions for upsampling
+- ReLU activation in hidden layers
+- Sigmoid output activation for pixel reconstruction.
 
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