Awesome
The repository gives the trained models MoreMNAS A,B,C and D.
How to reproduce & calculate metrics
$ python calculate.py --pb_path ./pretrained_model/MoreMNAS-A.pb
--save_path ./result/
Comparison of some state-of-the-art SR Models
| Method | MulAdds | Params | Set5 | Set14 | BSD100 | Urban100 |
|---|---|---|---|---|---|---|
| SRCNN | 52.7G | 57K | 36.66/0.9542 | 32.42/0.9063 | 31.36/0.8879 | 29.50/0.8946 |
| FSRCNN | 6.0G | 12K | 37.00/0.9558 | 32.63/0.9088 | 31.53/0.8920 | 29.88/0.9020 |
| VDSR | 612.6G | 665K | 37.53/0.9587 | 33.03/0.9124 | 31.90/0.8960 | 30.76/0.9140 |
| DRRN | 6,796.9G | 297K | 37.74/0.9591 | 33.23/0.9136 | 32.05/0.8973 | 31.23/0.9188 |
| MoreMNAS-A (ours) | 238.6G | 1039K | 37.63/0.9584 | 33.23/0.9138 | 31.95/0.8961 | 31.24/0.9187 |
| MoreMNAS-B (ours) | 256.9G | 1118K | 37.58/0.9584 | 33.22/0.9135 | 31.91/0.8959 | 31.14/0.9175 |
| MoreMNAS-C (ours) | 5.5G | 25K | 37.06/0.9561 | 32.75/0.9094 | 31.50/0.8904 | 29.92/0.9023 |
| MoreMNAS-D (ours) | 152.4G | 664K | 37.57/0.9584 | 33.25/0.9142 | 31.94/0.8966 | 31.25/0.9191 |
Qualitative results
Here are some results of MoreMNAS models vs. VDSR on Set 5. The complete result can be generated from the above mentions command.
Related Work
| method | url | language | Official |
|---|---|---|---|
| SRCNN | http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html | Matlab, Caffe | Yes |
| FSRCNN | http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html | Matlab, Caffe | Yes |
| DRRN | https://github.com/tyshiwo/DRRN_CVPR17 | Caffe | Yes |
| VDSR | https://github.com/twtygqyy/pytorch-vdsr | Pytorch | Yes |
Citation
Your citation is welcomed!
@article{chu2019multi,
title={Multi-objective reinforced evolution in mobile neural architecture search},
author={Chu, Xiangxiang and Zhang, Bo and Xu, Ruijun and Ma, Hailong},
journal={arXiv preprint arXiv:1901.01074},
year={2019}
}