The prediction of Yolov5

[cssDath]

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Hello! I would like to ask a few questions:

1. During the training process of yolov5, the call function of the ComputuLoss class in the loss. py file will generate an n value (n=b. shape [0]). Why does each epoch generate three different n values after completing a batch size training during training?
2. If I want to mathematically model the prediction results during the training process (i.e. xy of bbox),the pbox variable in loss. py stores thousands of tensors. Which part should I take values for as the location of the bbox?

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[github-actions]

👋 Hello @cssDath, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

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[glenn-jocher]

Hello! 😊

Great questions! Let's dive into them.

1. The variable n=b.shape[0] reflects the batch size for each forward pass in the training process. The reason you see three different n values per epoch could be due to the way batches are constructed, especially when using a varying batch size or when the final batch of an epoch has a different size (often smaller) than the others.

2. For modeling the prediction results (i.e. the bounding box coordinates) during training, the pbox tensor you're referring to contains predicted bounding boxes in the form [x_center, y_center, width, height]. If you want to specifically look at the prediction results, focus on the output of the model before it's passed into the loss computation. Typically, you can observe or log these values right after the forward pass through your model but before the loss function is calculated.

Remember, directly interacting with specific tensors and their shapes will require a good understanding of how the YOLOv5 architecture processes its inputs and outputs.

I hope this clarifies your questions! If you have any more, feel free to ask.

[github-actions]

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