YOLOv5 Output Size Issue

Question

YOLOv5 Output Size Issue

lllittleX opened this issue a month ago · 2 comments

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Question

I want to convert the results to ONNX first, and then to RKNN. However, when testing the ONNX model, I found that my output sizes are as follows:
torch.Size([1, 15120, 133])
torch.Size([1, 3, 48, 80, 133])
torch.Size([1, 3, 24, 40, 133])
torch.Size([1, 3, 12, 20, 133])
But from what I understand, the usual size is 80,80 instead of 48,80. I would like to ask if this is normal?

Additional

No response

Answer 1 · 2024-05-14T01:52:02.000Z

👋 Hello @lllittleX, 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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Answer 2 · 2024-05-14T07:46:43.000Z

@lllittleX hello! Thanks for reaching out with your question about the output size from the YOLOv5 model when converted to ONNX.

The output sizes you are seeing (e.g., torch.Size([1, 3, 48, 80, 133])) depend on the input image size you are using and the architecture of the model. Normally, YOLOv5 uses a grid that scales down from the input size by factors of 32, 16, and 8. If your input size isn't a multiple of these, the resulting grid sizes could appear unconventional, as is likely the case with a dimension of 80 instead of the usual 40 or 160, etc.

Ensure that your input image size is a multiple of the largest factor (generally 32) to maintain consistent grid dimensions across the different layers. You can adjust this in your initial configuration before training or inference.

Let's verify that input sizes and model configurations align with the expected outputs. Good luck with your conversions! 🚀