| --- |
| tags: |
| - onnx |
| - image-classification |
| - cifar10 |
| - dropout |
| - aidge |
| pipeline_tag: image-classification |
| datasets: |
| - cifar10 |
| metrics: |
| - accuracy |
| model-index: |
| - name: Custom ResNet-18 with integrated Dropout layers |
| results: |
| - task: |
| type: image-classification |
| name: Image Classification |
| dataset: |
| name: CIFAR-10 |
| type: cifar10 |
| metrics: |
| - type: accuracy |
| value: 83.96% |
| language: |
| - en |
| base_model: |
| - resnet-18 |
| --- |
| |
| # Custom ResNet-18 with integrated Dropout Layers |
|
|
| This is a **custom ResNet-18** ONNX model implemented in **PyTorch** with integrated **Dropout** layers. |
| It was trained on the **CIFAR-10** dataset for image classification tasks. |
| The model has been exported using **opset version 15** and is fully compatible with the **Aidge** platform. |
|
|
| ## Details |
|
|
| - **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 |
|
|
| --- |
