Initialization of yolov8

[AakiraOtok]

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Question

I found that YOLOv8 does not use initialization weights. It is possible that initialization weights were used in earlier versions, but subsequent experiments showed their inefficiency. Could you provide more information about the reasons behind this decision?

def initialize_weights(model):
    """Initialize model weights to random values."""
    for m in model.modules():
        t = type(m)
        if t is nn.Conv2d:
            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif t is nn.BatchNorm2d:
            m.eps = 1e-3
            m.momentum = 0.03
        elif t in {nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU}:
            m.inplace = True

Additional

No response

[glenn-jocher]

Hello! Thanks for reaching out with your question about the initialization of weights in YOLOv8.

In YOLOv8, the decision to move away from specific weight initialization methods like Kaiming Normal was based on extensive testing and research. It was observed that modern deep learning models, especially with advancements in batch normalization and activation functions, are less sensitive to the initial weight values. The default PyTorch initializations (which are used in YOLOv8) have proven to be robust enough for training without the need for custom initialization schemes.

This simplifies the model architecture and can potentially reduce issues related to training stability and convergence. If you have specific needs or find that a different initialization works better for your particular dataset, you can certainly modify the initialize_weights function as needed.

Here's a quick example if you decide to use a custom initialization:

def initialize_weights(model):
    for m in model.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.constant_(m.weight, 1)
            nn.init.constant_(m.bias, 0)

Feel free to experiment and let us know if you have further questions! 😊