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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.

Comparison with VDSR

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}
}

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