Update README.md

README.md

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 # Custom ResNet-18 with integrated Dropout Layers
 
 This is a **custom ResNet-18** ONNX model implemented in **PyTorch** with integrated **Dropout** layers.
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 It was trained on the **CIFAR-10** dataset for image classification tasks. 
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 The model has been exported using **opset version 15** and is fully compatible with the **Aidge** platform.
 
 ## Details
 
-- **Architecture**: ResNet-18 (customized)
-- **Trained on**: CIFAR-10 (60,000 32x32 color images in 10 classes)
+- **Architecture**: Customized ResNet-18 with integrated Dropout layers
+- **Trained on**: CIFAR-10 (60,000 32x32 color images, 10 classes)
+- **Image Preprocessing**: Images were resized to `128×128`
+- **Data Normalization**: `mean = [0.4914, 0.4822, 0.4465]`, `std = [0.2023, 0.1994, 0.2010]`
+- **Dropout Probability**: 0.3
 - **ONNX opset version**: 15
 - **Conversion tool**: PyTorch → ONNX