How does YOLOv achieve multiple outputs?

HugTibers commented 1 year ago

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For example, I have a data set for behavior recognition, which is used to identify what behavior someone has done (stealing, smoking, lighting a lighter, etc.), and then I also have a data set for fall recognition. How can I recognize falls while recognizing behaviors? Because when a person is smoking, he may also fall. If it is a general model, he will take a maximum value between the confidence of smoking and falling to judge whether it is smoking or falling, but I want to achieve The most important thing is that in addition to recognizing that the person is smoking, it is also necessary to recognize that the person has fallen.(Google Translate )

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glenn-jocher commented 1 year ago

@HugTibers yOLOv5 achieves multiple outputs through its architecture and the way it processes input data. In YOLOv5, the network is designed to have multiple detection heads, each responsible for detecting different object classes.

To recognize falls while recognizing behaviors, you can train your model on a dataset that includes both behaviors and falls. During training, YOLOv5 will learn to detect and classify different object classes simultaneously. The model will produce multiple outputs, each corresponding to a specific class, including falls and behaviors.

During inference, the model will provide predictions for each class, including confidence scores. You can then process these predictions based on your specific use case. For example, you can set a threshold for each class to determine if an object is falling, performing a specific behavior, or both.

Keep in mind that YOLOv5 is a powerful and flexible tool, but its performance and accuracy depend greatly on the quality and diversity of the training data. Make sure to provide sufficient training examples for falls and behaviors to achieve the desired recognition performance.

If you have any further questions or need assistance with training or implementation, feel free to ask. The YOLOv5 community and the Ultralytics team are here to help.

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