Mixed Link Networks

MixNet: [Arxiv]

by Wenhai Wang, Xiang Li, Jian Yang, Tong Lu

IMAGINE Lab@National Key Laboratory for Novel Software Technology, Nanjing University.
DeepInsight@PCALab, Nanjing University of Science and Technology.

Requirements

• Install PyTorch v0.2.0
• Clone recursively

git clone --recursive https://github.com/DeepInsight-PCALab/MixNet.git

• Download the ImageNet dataset and move validation images to labeled subfolders
  • To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh

Training

CIFAR-10

CUDA_VISIBLE_DEVICES=0 python cifar.py --dataset cifar10 --depth 100 --k1 12 --k2 12 --train-batch 64 --epochs 300 --schedule 150 225 --wd 1e-4 --gamma 0.1 --checkpoint checkpoints/cifar10/mixnet-100/

ImageNet

CUDA_VISIBLE_DEVICES=0,1,2,3 python imagenet.py -d ../imagenet/ -j 4 --arch mixnet105 --train-batch 200 --checkpoint checkpoints/imagenet/mixnet-105/

Testing on ImageNet

CUDA_VISIBLE_DEVICES=0 python imagenet.py -d ../imagenet/ -j 4 --arch mixnet105 --test-batch 20 --pretrained pretrained/mixnet105.pth.tar --evaluate

Results on CIFAR

Results on ImageNet and Pretrained Models

Citation

@inproceedings{wang2018mixed,
  title={Mixed link networks},
  author={Wang, Wenhai and Li, Xiang and Lu, Tong and Yang, Jian},
  booktitle={Proceedings of the 27th International Joint Conference on Artificial Intelligence},
  pages={2819--2825},
  year={2018},
  organization={AAAI Press}
}