Batch size in loss function

AlexeyDate commented 1 year ago

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Thank you for such a useful repo! I try to understand some features and I am thinking about calculation loss function. Why are you multiplying the calculated values by the batch size in this line: return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() I think you need to divide all values per batches to get the averages, no?

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glenn-jocher commented 1 year ago

@AlexeyDate thank you for your kind words and for reaching out with your question! I appreciate your interest in understanding the loss function calculation in the YOLOv3 repository.

The multiplication of the loss values by the batch size in the mentioned line (return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()) does not aim to calculate averages.

In object detection tasks, the loss function is typically computed per image. However, during training, it is common to process multiple images in a batch simultaneously. To properly scale the loss contribution from the batch, the loss values are multiplied by the batch size.

This scaling is done to ensure that gradients and updates to the model's parameters are appropriately adjusted when considering a batch of samples instead of a single sample. Dividing the loss values by the batch size would underestimate the impact of the loss on the overall model update.

I hope this clarifies the purpose of multiplying the loss values by the batch size. If you have any further questions or need additional assistance, please feel free to ask.

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