RandWireNN(Randomly Wired Neural Network)
PyTorch implementation of : Exploring Randomly Wired Neural Networks for Image Recognition.
- 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.
|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.
- python packages
- pytorch = 0.4.1
Download the ImageNet dataset and put them into the
Training a model from scratch
All materials in this repository are released under the Apache License 2.0.