YOLO
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From Object Detection to Video Intelligence
Read Full Article: From Object Detection to Video Intelligence
Object detection models like YOLO excel at real-time, frame-level inference and producing clean bounding box outputs, but they fall short when it comes to understanding video as data. The limitations arise in system design rather than model performance, as frame-level predictions do not naturally support temporal reasoning, nor do they provide a searchable or queryable representation. Additionally, audio, context, and higher-level semantics are often disconnected, highlighting the difference between identifying objects in a frame and understanding the events in a video. The focus needs to shift towards building pipelines that incorporate temporal aggregation, multimodal fusion, and systems that enhance rather than replace models. This approach aims to address the complexities of video analysis, emphasizing the need for both advanced models and robust systems. Understanding these limitations is crucial for developing comprehensive video intelligence solutions.
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Training a Custom YOLO Model for Posture Detection
Read Full Article: Training a Custom YOLO Model for Posture Detection
Embarking on a machine learning journey, a newcomer trained a YOLO classification model to detect poor sitting posture, discovering valuable insights and challenges. While pose estimation initially seemed promising, it failed to deliver results, and the YOLO model struggled with partial side views, highlighting the limitations of pre-trained models. The experience underscored that a lower training loss doesn't guarantee a better model, as evidenced by overfitting when validation accuracy remained unchanged. Utilizing the early stopping parameter proved crucial in optimizing training time, and converting the model from .pt to TensorRT significantly improved inference speed, doubling the frame rate from 15 to 30 FPS. Understanding these nuances is essential for efficient and effective model training in machine learning projects.