Awesome
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% <sup>*</sup> | 100 | cosine lr | 5e-5 |
| RandWire-WS(4, 0.75), C=78 | 74.7% | 73.97% <sup>*</sup> | 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.