Chris Mair
Software Entwicklung und Schulung
News
News vom 3.12.2020
Developer's Thursday Meeting (NOI Techpark) on Deep Learning
In this event I showed a few experiments with machine learning.
The example application was classifying traffic signs. The data set was taken
from
"The German Traffic Sign Benchmark" (GTSRB): it contains 51839 images of
43 different traffic signs from real-world captures (39209 for training and
12630 for the benchmark).
I used
GNU Octave to preprocess the images (make the size uniform, stretch the
dynamic range, convert to grey scale, store everything in a simple .mat-file).
In the first experiment I took a simple fully-connected network with one hidden
layer with 1000 nodes. I implemented the net in GNU Octave using the math from
Tariq Rashid's "Make Your Own Neural Network". With this net I could get a ~
85% correct detection rate in the benchmark.
In the next experiment I used dlib, which
is a machine learning toolkit for C++. The same network architecture
implemented using dlib gave a correct detection rate of ~ 89.5% with dlib's
more sophisticated training algorithm.
In the last experiment I kept using dlib, but changed the network architecture
to a "deep" network: I started from a
"LeNet" architecture and added a few more layers, mixing fully-connected,
convolutional and max-pooling layers. To train this network I used a VM with
a Nvidia Tesla V100, which dlib can use for CUDA/CuDNN. This way the correct
detection rate in the benchmark increased to ~ 95%. Not bad for just a few
lines of C++ code and 30 seconds of training.
To put this into perspective, when
the first
competition launched in 2011 a score of 95% would score about half way in
the field of participants, with the top place reaching 98.98% and beating human
performance at 98.81%.
→ download the source code [zip]
News vom 22.7.2020, aktualisiert am 21.8.2020
Some (humble) Experiments in Time Series Forecasting
→ aktualisierte Präsentation [pdf]
→ aktualisierter Quellcode, Daten und Bilder zum Download [zip]