In the YOLOv5 target detection algorithm, the purpose of normalizing the input layer data is to map the pixel value range of the input image to a smaller interval, usually between 0 and 1. This normalization process has the following effects:
- 1. Constrain the data range: Normalization can ensure that the input data is in the same scale range, and avoid excessive differences in pixel values between different images. This helps the network model to better learn image features, improving the stability and convergence speed of the model.
- 2. Improve the optimization process: Normalizing the input data can help the optimization algorithm converge faster. The normalized data distribution is closer to the uniform distribution, which helps to avoid the optimization algorithm from falling into an unstable situation during the training process.
- 3. Prevent gradient explosion and gradient disappearance: If the pixel value of the input data is too large or too small, it may cause gradient explosion or gradient disappearance, making the model unable to learn effectively. By normalizing the input data to a suitable range, these problems can be reduced and the stability of the gradient can be improved.
- 4. Speed up the training process: The normalized input data can better adapt to the learning rate of the optimization algorithm, making the training process more efficient. In addition, normalization can also reduce the computational complexity during the training process and speed up the training speed of the model.
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To sum up, normalizing the input layer data can improve the training stability of the model, accelerate the optimization process, and improve the performance and effectiveness of the object detection algorithm.
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