This project is a Real-Time Vehicle and Pedestrian Detection System that uses the YOLOv9 model to detect objects, estimate distances, and issue alerts. It enhances driving safety by providing real-time feedback through a user-friendly interface and robust backend processing.
A custom neural network architecture built using particle physics, where each particle acts as it's own individual "neuron" in the network; with their own distinct roles, variables, connections, and more. The underlying goal is to alleviate the need for fine-tuning, explore emergent behavior, and enable unique and autonomous artificial intelligence
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