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Tensorflow implementation of pixel-recursive-super-resolution(Google Brain paper: https://arxiv.org/abs/1702.00783)
| Date | Stars |
|---|---|
| 2026-07-24 | 480 |
| 2026-07-25 | 480 |
| 2026-07-28 | 480 |
| 2026-07-30 | 480 |
| 2026-09-18 | 479 |
| 2026-09-20 | 479 |
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# Pixel Recursive Super Resolution
TensorFlow implementation of [Pixel Recursive Super Resolution](https://arxiv.org/abs/1702.00783). This implementation contains:
## Requirements
- Python 2.7
- [Skimage](http://scikit-image.org/)
- [TensorFlow](https://www.tensorflow.org/) 1.0
## Usage
First, download data [celebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
$ mkdir data
$ cd data
$ ln -s $celebA_path celebA
Then, create image_list file:
$ python tools/create_img_lists.py --dataset=data/celebA --outfile=data/train.txt
To train model on gpu:
$ python tools/train.py
(or $ python tools/train.py --device_id=0)
To train model on cpu:
$ python tools/train.py --use_gpu=False
## Samples
Training after 30000 iteration.
## Training details
cross entropy loss:
## Author
nilboy / [@nilboy](https://github.com/nilboy)Excerpt of 1,001 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:365d53613c954481, topic:super-resolution, name:super resolution, desc:super resolution
matched fp:365d53613c954481, topic:tensorflow