| --- |
| license: cc |
| tags: |
| - onnx |
| - image-classification |
| - cifar10 |
| - dropout |
| - aidge |
| pipeline_tag: image-classification |
| datasets: |
| - cifar10 |
| metrics: |
| - type: accuracy |
| value: 69.96% |
| model-index: |
| - name: Custom ResNet-18 with Integrated Dropout |
| results: |
| - task: |
| type: image-classification |
| name: Image Classification |
| dataset: |
| name: CIFAR-10 |
| type: cifar10 |
| metrics: |
| - type: accuracy |
| value: 69.96% |
| --- |
| |
| # CustomCNN (ONNX) |
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| This is a **custom convolutional neural network (CNN)** trained on the **CIFAR-10** dataset, developed to test the integration of a **custom Dropout operator** for the **Aidge** platform. |
|
|
| ## Details |
|
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| - **Architecture**: Custom Convolutional Neural Network (CNN) with a Dropout layer |
| - **Trained on**: CIFAR-10 (60,000 32x32 color images, 10 classes) |
| - **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 |
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