MSDNet with Learning to Cascade

This is a pytorch implementation of the following paper [AAAI] [arXiv]:

@inproceedings{enomoto2021ltc,
  title={Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems},
  author={Shohei Enomoto and Takeharu Eda},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  pages={7331--7339},
  year={2021}
}

Please read license.txt before reading or using the files.

Dependencies:

• Python3
• PyTorch >= 0.4.0

Network Configurations

Train an MSDNet (block=2, step=2, base=4) on CIFAR-100:

python3 main.py --data-root /PATH/TO/CIFAR100 --data cifar100 --save /PATH/TO/SAVE \
                --arch msdnet --batch-size 64 --epochs 300 --nBlocks 2 \
                --stepmode lin_grow --step 2 --base 4 --nChannels 16 \
                -j 16 --use-valid --seed 0

Train an MSDNet (block=2, step=2, base=4) with LtC(w=1.5, C=0.5) on CIFAR-100:

python3 main.py --data-root /PATH/TO/CIFAR100 --data cifar100 --save /PATH/TO/SAVE \
                --arch msdnet --batch-size 64 --epochs 300 --nBlocks 2 \
                --stepmode lin_grow --step 2 --base 4 --nChannels 16 \
                -j 16 --use-valid --seed 0 --ltc --w 1.5 --C 0.5

Test an MSDNet (block=2, step=2, base=4) on CIFAR-100:

python3 main.py --evalmode dynamic --batch-size 128 \
                --evaluate-from /PATH/TO/save_models/model_best.pth.tar \
                --arch msdnet --data cifar100 --data-root /PATH/TO/CIFAR100 --nBlocks 2 --step 2 --base 4 \
                --nChannels 16 --stepmode lin_grow --use-valid --seed 0