| | --- |
| | 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 |
| |
|
| | --- |
