{tag=PyTorch model}

https://github.com/activatedgeek/LeNet-5

Good packaging! Tested on <Ubuntu 22.10>:
``
git clone https://github.com/activatedgeek/LeNet-5
cd LeNet-5
git checkout 95b55a838f9d90536fd3b303cede12cf8b5da47f
virtualenv -p python3 .venv
. .venv/bin/activate

# Their requirements.txt uses >= and some == are incompatible with our Ubuntu.
pip install
  Pillow==6.2.0 \
  numpy==1.24.2 \
  onnx==1.13.1 \
  torch==2.0.0 \
  torchvision==0.15.1 \
  visdom==0.2.4 \
;

time python run.py
``
This throws a billion exceptions because we didn't start the visdom server, but never mind that.

The scrip does a fixed 15 <epoch (deep learning)>[epochs].

Output on <ciro santilli s hardware/P51>:
``
real    2m10.262s
user    11m9.771s
sys     0m26.368s
``

The run also produces a `lenet.onnx` <ONNX> file, which is pretty neat, and allows us for example to visualize it on <Netron>:

\Image[https://raw.githubusercontent.com/cirosantilli/media/e9225ddf4bb8ce4bad8cc2a9d6503d683dec5db6/activatedgeek_LeNet-5_onnx.svg]
{title=<Netron> visualization of the <activatedgeek LeNet-5> <ONNX> output}
{description=
From this we can see the bifurcation on the computational graph as done in the code at:
``
output = self.c1(img)
x = self.c2_1(output)
output = self.c2_2(output)
output += x
output = self.c3(output)
``
This doesn't seem to conform to the original <LeNet-5> however?
}


Discussion on "activatedgeek/LeNet-5" by Ciro Santilli 32