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MSRResNetx4_model Add files via upload Jul 17, 2019
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README.md Update README.md Jul 26, 2019
SRResNet.py Add files via upload Jul 17, 2019
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README.md

AIM Workshop and Challenge @ ICCV 2019

Constrained Super-Resolution Challenge

Jointly with AIM workshop we have an AIM challenge on Constrained 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 MSRResNet (Ledig et al, 2017 & Wang et al, 2018).

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 MSRResNet (Ledig et al, 2017 & Wang et al, 2018) 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 MSRResNet (Ledig et al, 2017 & Wang et al, 2018) in terms of number of parameters and inference time on a common GPU (ie. Titan Xp).

Baseline model (MSRResNet)

  • Number of parameters: 1,517,571 (1.5M)

    number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
  • Average PSNR on validation data: 29.00 dB

  • Average inference time (Titan Xp) on validation data: 0.170 second

    Note: I selected the best average inference time among three trials

Run test_demo.py to test the model

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