../

This post highlights several artificial neural networks I've built using Darknet[1], DarkHelp[2], and DarkMark.

Project Notes Samples Images
Mailboxes Custom artificial neural network trained to recognize 17 classes related to Canada Post mail boxes:
  • numbers 1 to 16
  • lock
Image source: self.
Click on the image to view the gallery.
Street Signs Custom artificial neural network trained to recognize 5 classes related to common street signs:
  • stop sign
  • yield sign
  • street name
  • speed limit
  • back of stop sign
Image source: self.
Click on the image to view the gallery.
Store Price Labels Custom artificial neural network trained to recognize 12 classes related to price tags:
  • digits 0 to 9
  • barcode
  • description
Image source: self.
Click on the image to view the gallery.
Dog Versus Cat Custom artificial neural network trained to recognize just 2 classes:
  • dogs
  • cats
Image source: Kaggle "Dogs and Cats" project.

See also: Dogs And Cats, a post I wrote with some interesting results during the training of this neural network.
Click on the image to view the gallery.
Cancer Cells Custom artificial neural network trained to recognize 15 different types of cancer cells:
  • Basophil
  • Eosinophil
  • Erythroblast
  • Lymphocyte (atypical)
  • Lymphocyte (typical)
  • Metamyelocyte
  • Monoblast
  • Monocyte
  • Myeloblast
  • Myelocyte
  • Neutrophil (band)
  • Neutrophil (segmented)
  • Promyelocyte
  • Promyelocyte (bilobled)
  • Smudge cell
Image source: A Single-cell Morphological Dataset of Leukocytes from TCIA (The Cancer Image Archive).
Click on the image to view the gallery.
Video Custom artificial neural network trained to recognize 7 different classes, which is then applied to a video stream:
  • eye, nose, mouth, ear, bottle, blue pen, orange pen
YOLOv3 neural network was trained using the Darknet framework.
  • Training (10K iterations) took 1.5 hours. Weight files are 33 MiB in size.
  • Video capture and neural network image processing is written in C++.
  • In this video, it is running in a VirtualBox VM without a GPU at ~7 FPS.

See the series of posts I wrote on building your own neural network, or email me if you want to discuss your specific neural net requirements.

Last modified: 2019-11-05
Stéphane Charette, stephanecharette@gmail.com
../