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And this is the architecture of the YOLOv8.
YOLOv8 follows the single-shot detection paradigm, it means that processes the entire image in a single forward pass to predict bounding boxes and class probabilities.
The YOLOv8 architecture comprises multiple backbone networks, like CSPDarkNet.
To providing users with flexibility based on computational resources and performance requirements.
These backbone networks are integrated with a neck and head architecture, enhancing feature extraction and bounding box prediction.
One of the notable features of YOLOv8 is its anchor-based approach to predicting bounding boxes.
YOLOv8 also introduces a novel ensemble approach, allowing users to combine different model variants to achieve superior performance.
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IEEE/ICACT20240034 Slide.07
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