| --- |
| datasets: |
| - ylecun/mnist |
| metrics: |
| - accuracy |
| pipeline_tag: image-classification |
| library_name: torch |
| --- |
| |
| # LeNet |
|
|
| Toy model mainly used to showcase Aidge in various tutorial, for example: https://eclipse.dev/aidge/source/Tutorial/101_first_step.html |
|
|
| ## Aidge support |
|
|
| > Note: We tested this network for the following features. If you encounter any error please open an [issue](https://gitlab.eclipse.org/groups/eclipse/aidge/-/issues). Features not tested in CI may not be functional. |
|
|
| | Feature | Tested in CI | |
| | :---------: | :----------: | |
| | ONNX import | ✔ | |
| | Backend CPU | ❌ | |
| | Export CPP | ❌ | |
|
|
| ## MNIST |
|
|
| * **Input** |
| * size: [N, 1, 28, 28] |
| * format: [N, C, H, W] |
| * preprocessing: `None` |
| * **Output** |
| * size: [N, 10] |
|
|
| ### ONNX attributes |
|
|
| * Opset: <opset> |
| * Source: PyTorch |
| * Operators: 22 (6 types) |
| - Conv2D: 2 |
| - FC: 3 |
| - Flatten: 1 |
| - MaxPooling2D: 2 |
| - Producer: 10 |
| - ReLU: 4 |
|
|
| ### Benchmark |
|
|
| > Coming soon |
