Residual Local Feature Network
Our team (ByteESR) won the first place in Runtime Track (Main Track) and the second place in Overall Performance Track (Sub-Track 2) of NTIRE 2022 Efficient Super-Resolution Challenge.
| model | Runtime[ms] | Params[M] | Flops[G] | Acts[M] | GPU Mem[M] |
|---|---|---|---|---|---|
| RLFN_ntire | 27.11 | 0.317 | 19.70 | 80.05 | 377.91 |
Open-Source
For commercial reasons, we don't release training code temporarily, please refer to EDSR framework and our paper for details.
- Paper of our method [arXiv]
- Report of our performance [NTIRE22 official report]
- The pretrained model and test code in challenge.
Testing
We modified the official test code. To reproduce our result in the ESR challenge, please install PyTorch >= 1.5.0.
run python test_demo.py to generate image results.
All test results will be saved in the folder data/DIV2K_test_LR_results