 activatedgeek/LeNet-5 use ONNX for inference

ID: activatedgeek-lenet-5-use-onnx-for-inference

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activatedgeek/LeNet-5 use ONNX for inference by

Ciro Santilli 35  Updated 2025-03-25  +Created 1970-01-01

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:

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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