![]() RetinaNet with ResNet50 backbone to be specific.ĥ. Train the detector model with Detectron 2 framework.Annotate the images using Labelme open-source annotator.Get 2–3 videos from youtube that contain keywords “driving, tbilisi”, “driving georgia”.Or, worse - annotating the dataset for the detector model in COCO format.Īfter a couple of minutes of thinking, I came up with an approach, that would take the minimum time possible to get some results. For instance: labeling dataset (not a fan, right?). I’m pretty interested in challenging myself to do some work only by myself, even if it includes some menial work to do. While looking around the web, I couldn’t find any collection, or at least a ready-to-be-scrapped place, where I could get a dataset of Georgian number plates. Robust, real-time detection of number plates is not an unresolved challenge for the specific countries, that have dataset published. Most (well, at least some) of the ‘Smart Cameras’ don’t use number plate detection & recognition systems, but they pay attention to specific hidden codes on the number plates, and by sticking some transparent material on them, tricking the intelligent systems are not just possible, but pretty easy.
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