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What is YOLOv5: A deep look into the internal features of the popular object detector
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This study presents a comprehensive analysis of the YOLOv5 object detection model, examining its architecture, training methodologies, and performance. Key components, including the Cross Stage Partial backbone and Path Aggregation-Network, are explored in detail. The paper reviews the model's performance across various metrics and hardware platforms. Additionally, the study discusses the transition from Darknet to PyTorch and its impact on model development. Overall, this research provides insights into YOLOv5's capabilities and its position within the broader landscape of object detection and why it is a popular choice for constrained edge deployment scenarios.
Forward citations
Cited by 8 Pith papers
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A review of YOLOv12's attention-based architecture and its benchmarks, with all performance numbers sourced from the original YOLOv12 paper rather than new experiments.
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