Comments (1)
Hi,
We first reduced the computational complexity in BiO-Net by optimizing the feature fusion scheme and feature channel numbers at each level. Such optimizations are adopted in the design of both BiO-Net++ and BiX-Net yielding only 0.43 M trainable parameters. Our Phase1 search algorithm provides a further 8.6% MACs reduction, and our BiX-Net eventually achieves a 20% MACs reduction compared to BiO-Net++.
If you check the code, you can find that we constrained the channel number for each scale and avoid using concatenation (use weighted addition instead) when fusing the skips.
Thank you for your question.
Regards,
from bix-nas.
Related Issues (5)
- About the channels HOT 1
- GPU memory cost and reported MACs. HOT 1
- Hi, thanks for your outstanding work and would you give me any suggestions how to resize the image size during the training phase such (224, 244). Besides, your iaa augmentation technique technique don't work for me to resize the image. Further, i tried different augmentation techniques to resize the image size but unfortunately received the errors from dataloaders.
- Hi, can you please share the CHOSE dataset preprocessing files or processed dataset on my email [email protected]?
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from bix-nas.