SuperSAM: Crafting a SAM supernetwork via structured pruning and unstructured parameter prioritization
This is the official implementation for the paper:
Train supernet on SA1B dataset.
python nas.py --dataset=sa1b \
--epochs=5 --batch_size=8 --trainable=em \
--lr=1e-5 --weight_decay=0 --train_subset=400 \
--test_subset=100 --train_prompt=p \
--test_prompt=p --loss=dice --sandwich=lsm \
--no_verbose --save_interval=4 \Anonymous
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The experiments of this work is sponsored by [anonymous institution] and [anonymous institution].