Now let's try and use the trained <ONNX> file for inference on some manually drawn images on <GIMP>:

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!

\Image[https://raw.githubusercontent.com/cirosantilli/media/master/home/python/pytorch/resnet_demo_out.jpg]
{title=Number 9 drawn with mouse on <GIMP> by <Ciro Santilli> (2023)}

We can try the code adapted from https://thenewstack.io/tutorial-using-a-pre-trained-onnx-model-for-inferencing/[] at \a[python/onnx_cheat/infer_mnist.py]:
``
cd python/onnx_cheat
./infer_mnist.py lenet.onnx infer_mnist_9.png
``
and it works pretty well! The protram outputs:
``
9
``
as desired.

We can also try with images directly from <Extract MNIST images>.
``
for f in /home/ciro/git/mnist_png/out/testing/1/*.png; do echo $f; infer.py $f ; done
``
and the accuracy is great as expected.


Activatedgeek lenet 5 use onnx for inference

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