Building an AI-powered Bot to Beat the Chrome Dino Game

Erik Kokalj's blog post details the creation of an AI-powered bot designed to beat the Chrome Dino game using an object detection model. The project involves several key steps, beginning with data collection using the mss library to capture screenshots of the game, which are then annotated manually and through Roboflow's AI Labeling tool to identify game objects like "Dino," "Cactus," and "Bird." The annotated dataset is used to fine-tune a YOLO model via Roboflow, selecting the "YOLO8 - Fast" architecture for training due to its efficiency with distinct objects. Once trained, the model is deployed locally using Roboflow's Inference library to interpret live game screenshots and send commands to control the Dino's actions based on detected obstacles. This process is facilitated by the pynput library, which allows the bot to execute jumps or ducks as necessary, demonstrating a complete computer vision project lifecycle from data gathering to deployment.

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