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