RandWireNN(Randomly Wired Neural Network)

PyTorch implementation of : Exploring Randomly Wired Neural Networks for Image Recognition.

Update

• 2019/4/10: Release a result of regular computation(C=109) RandWird-WS(4,0.75). It has Top-1 accuracy of 77.07% on Imagenet dataset.
• 2019/4/7: The code of RandWireNN are released.

  Reproduced results

  Model	Paper's Top-1	Mine Top-1	Epochs	LR Scheduler	Weight Decay
  RandWire-WS(4, 0.75), C=109	79%	77% *	100	cosine lr	5e-5
  RandWire-WS(4, 0.75), C=78	74.7%	73.97% *	250	cosine lr	5e-5

*This result does not take advantage of dropout, droppath and label smoothing techniques. I will use these tricks to retrain the model.

Requirements

• python packages
  • pytorch = 0.4.1
  • torchvision>=0.2.1
  • tensorboardX
  • pyyaml
  • CVdevKit
  • networkx

Data Preparation

Download the ImageNet dataset and put them into the {repo_root}/data/imagenet.

Training a model from scratch

./train.sh configs/config_regular_c109_n32.yaml

License

All materials in this repository are released under the Apache License 2.0.