/IMDN

Primary LanguagePython

NTIRE 2022 Workshop and Challenge @ CVPR 2022

Efficient Super-Resolution Challenge

Jointly with NTIRE workshop we have a challenge on Efficient Super-Resolution, that is, the task of super-resolving (increasing the resolution) an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The challenge has three tracks.

Track 1: Parameters, the aim is to obtain a network design / solution with the lowest amount of parameters while being constrained to maintain or improve the PSNR result and the inference time (runtime) of IMDN (Hui et al, 2017).

Track 2: Inference, the aim is to obtain a network design / solution with the lowest inference time (runtime) on a common GPU (ie. Titan Xp) while being constrained to maintain or improve over IMDN (Hui et al, 2017) in terms of number of parameters and the PSNR result.

Track 3: Fidelity, the aim is to obtain a network design / solution with the best fidelity (PSNR) while being constrained to maintain or improve over IMDN (Hui et al, 2017) in terms of number of parameters and inference time on a common GPU (ie. Titan Xp).

Baseline model (IMDN)

  • Number of parameters: 893,936 (0.89M)

    number_parameters = sum(map(lambda x: x.numel(), model.parameters()))

  • Average PSNR on validation data: 29.13 dB

  • Average inference time (Titan Xp) on validation data: 0.10 second

    Note: The best average inference time among three trials is selected.

Run test_demo.py to test the model

How to use the code during test phase.

  1. git clone https://github.com/ofsoundof/IMDN
  2. Put your model script under the models folder.
  3. Put your pretrained model under the model_zoo folder.
  4. Modify model_path in test_demo.py. Modify the imported models.
  5. python test_demo.py