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--- |
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tags: |
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- onnx |
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- image-classification |
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- cifar10 |
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- dropout |
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- aidge |
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pipeline_tag: image-classification |
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datasets: |
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- cifar10 |
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metrics: |
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- accuracy |
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model-index: |
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- name: Custom ResNet-18 with integrated Dropout layers |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: CIFAR-10 |
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type: cifar10 |
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metrics: |
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- type: accuracy |
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value: 83.96% |
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language: |
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- en |
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base_model: |
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- resnet-18 |
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--- |
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# Custom ResNet-18 with integrated Dropout Layers |
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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. |
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## Details |
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- **Architecture**: Customized ResNet-18 with integrated Dropout layers |
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- **Trained on**: CIFAR-10 (60,000 32x32 color images, 10 classes) |
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- **Image Preprocessing**: Images were resized to `128×128` |
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- **Data Normalization**: `mean = [0.4914, 0.4822, 0.4465]` ; `std = [0.2023, 0.1994, 0.2010]` |
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- **Dropout Probability**: 0.3 |
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- **ONNX opset version**: 15 |
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- **Conversion tool**: PyTorch → ONNX |
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--- |
