This is a reimplementation of Exploring Randomly Wired Neural Networks for Image Recognition
- Python 3.5
- PyTorch==1.0.0
- sklearn, tensorboardX, numpy
ws: python graph/ws.py -k -p
er: python graph/er.py -p
ba: python graph/ba.py -m
python train.py --data <path to ImageNet>
--regime <small is True, regular is False>
--base_channels <78, 109, 154>
python eval.py --data <path to ImageNet>
--regime <small is True, regular is False>
--base_channels <78, 109, 154>
--model_path <path to trained path
Validation result on Imagenet(ILSVRC2012) dataset:
Top 1 accuracy (%) | Paper | Here |
---|---|---|
RandWire-WS(4, 0.75), C=78 | 74.7 | 70.0 |
Xie S, Kirillov A, Girshick R, et al. Exploring randomly wired neural networks for image recognition[J]. arXiv preprint arXiv:1904.01569, 2019.
Seungwon Park