Comments (2)
When running the model, you should see that the re-parameterized model has a lower parameter count (and reduced number of layers) when compared to the base model. From my experiments, the effect of re-parameterization on the parameter count is marginal for P5 models (YOLOv7-tiny to YOLOv7-X) but significantly more important for YOLOv7-W6 and its variants.
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Do you have a sense of what magnitude inference time improvement one should expect from reparameterizing the YOLOv7 default P5 model? Is what I'm seeing (basically zero) reasonable or does it suggest I'm doing something wrong?
Thank you!
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