activatedgeek/LeNet-5 use ONNX for inference Updated 2025-03-28 +Created 1970-01-01
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Now let's try and use the trained ONNX file for inference on some manually drawn images on GIMP:
Figure 1.
Number 9 drawn with mouse on GIMP by Ciro Santilli (2023)
Note that:
  • the images must be drawn with white on black. If you use black on white, it the accuracy becomes terrible. This is a good very example of brittleness in AI systems!
  • images must be converted to 32x32 for lenet.onnx, as that is what training was done on. The training step converted the 28x28 images to 32x32 as the first thing it does before training even starts
We can try the code adapted from thenewstack.io/tutorial-using-a-pre-trained-onnx-model-for-inferencing/ at lenet/infer.py:
cd lenet
cp ~/git/LeNet-5/lenet.onnx .
wget -O 9.png https://raw.githubusercontent.com/cirosantilli/media/master/Digit_9_hand_drawn_by_Ciro_Santilli_on_GIMP_with_mouse_white_on_black.png
./infer.py 9.png
and it works pretty well! The program outputs:
9
as desired.
We can also try with images directly from Extract MNIST images.
infer_mnist.py lenet.onnx mnist_png/out/testing/1/*.png
and the accuracy is great as expected.
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@cirosantilli/_file/lenet Updated 2025-03-28 +Created 2025-03-17
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This is a small fork of activatedgeek/LeNet-5 by Ciro Santilli adding better integration and automation for:
Install on Ubuntu 24.10:
sudo apt install protobuf-compiler
cd lenet
virtualenv -p python3 .venv
. .venv/bin/activate
pip install -r requirements-python-3-12.txt
Download and extract MNIST train, test accuracy, and generate the ONNX lenet.onnx:
./train.py
Extract MNIST images as PNG:
./extract_pngs.py
Infer some individual images using the ONNX:
./infer.py data/MNIST/png/test/0/*.png
Draw on a GUI and see live inference using the ONNX:
./draw.py
TODO: the following are missing for this to work:
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