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Pytorch Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"
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configs randwireNN Apr 5, 2019
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__init__.py
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train.sh randwireNN Apr 5, 2019
utils.py randwireNN Apr 5, 2019

README.md

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.

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