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This Python script generates a synthetic dataset of traffic sign images in COCO format, intended for training and testing object detection models. The dataset includes various traffic sign overlays placed on diverse background images, offering a wide range of scenarios to enhance model robustness.
This program uses a deep neural network with several convolutional layers to classify traffic signs. The model is able to recognize traffic signs with an accuracy of 96,2%. It was trained and validated using the German Traffic Sign Dataset with 43 classes (types of traffic signs) and more than 50,000 images in total.
This is a Classifier Algorithm that can classify German Traffic-Signs. It uses the good old convolution network inspired by the Nvidia Model used in their self-driving car.