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We have a question about the ResNet pruning that when we run the ChipNet code and get these results.
Test Acc: 0.7316
Total Trainable Params: 317198
Channels pruned: 80.00%
Parameters pruned: 81.63%
Test Acc: 0.7556
Total Trainable Params: 853033
Channels pruned: 60.00%
Parameters pruned: 50.59%
We saw that the output channels for the blocks are not the same, so how can we get a smaller model with it?
Did you do padding to make sure they are the same?
The text was updated successfully, but these errors were encountered:
Hi,
We just came across your work, it was amazing!
We have a question about the ResNet pruning that when we run the ChipNet code and get these results.
Test Acc: 0.7316
Total Trainable Params: 317198
Channels pruned: 80.00%
Parameters pruned: 81.63%
Test Acc: 0.7556
Total Trainable Params: 853033
Channels pruned: 60.00%
Parameters pruned: 50.59%
We saw that the output channels for the blocks are not the same, so how can we get a smaller model with it?
Did you do padding to make sure they are the same?
The text was updated successfully, but these errors were encountered: