https://github.com/paparazz1/constrained_sr_model
https://github.com/paparazz1/constrained_sr_model
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/paparazz1/constrained_sr_model
- Owner: PaParaZz1
- Created: 2019-08-23T08:36:38.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-28T13:57:51.000Z (almost 6 years ago)
- Last Synced: 2025-01-05T21:12:15.377Z (5 months ago)
- Language: Python
- Size: 7.92 MB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## [AIM Workshop and Challenge](http://www.vision.ee.ethz.ch/aim19/) @ 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](https://competitions.codalab.org/competitions/20167)**, 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](https://competitions.codalab.org/competitions/20168)**, 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](https://competitions.codalab.org/competitions/20169)**, 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)
```python
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](test_demo.py) to test the model