ISWYDS exploring object detection using Darknet and YOLOv4 @Design Museum Gent
After repeating these steps for all our images we landed on 3000+ images featuring 37 classes (some images containing over 15 classes).

Picking your guns.

When it comes to object detection there are many options to pick from, but for our case, we will be using Darknet an open-source neural network framework written in C and CUDA to train our algorithm of choice; YOLOv4. You Only Look Once or YOLO is a state-of-the-art, real-time object detection system making R-CNN look stale, it is extremely fast, more than 1000x faster than R-CNN, and 100x faster than Fast R-CNN. Another good thing about YOLO is that it’s public domain and based on the license we can do whatever we want to do with it...🧐

YOLO LICENSE

Version 2, July 29 2016



THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY. NOW HERE'S THE REAL LICENSE:



0. Darknet is public domain.

1. Do whatever you want with it.

2. Stop emailing me about it!

That being said; after configuring our system and installing all the needed dependencies we can take her for a test ride — using some of the pre-trained algorithms that come out of the box, and see what she has to offer.

As we can tell from the results [pictures below], YOLOv4 comes with some pre-trained classes such as a chair, vase, and of course the very -uncanny- human. Let’s not make use of that last one, shall we? Although it missed out on some of the more obscure looking vases, we will be using these pre-trained weights as building blocks to create our very own. The goal here is to output new classes based on the object-number of the objects depicted.
["Olivier Van D'Huynslager"]
['AI', 'Museums', 'Design', 'Technology', 'Object Detection']