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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A PyTorch implementation of SRGAN based on CVPR 2017 paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
| Date | Stars |
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| 2026-07-24 | 1247 |
| 2026-07-25 | 1247 |
| 2026-07-28 | 1247 |
| 2026-07-30 | 1247 |
| 2026-08-06 | 1247 |
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# SRGAN A PyTorch implementation of SRGAN based on CVPR 2017 paper [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802). ## Requirements - [Anaconda](https://www.anaconda.com/download/) - PyTorch ``` conda install pytorch torchvision -c pytorch ``` - opencv ``` conda install opencv ``` ## Datasets ### Train、Val Dataset The train and val datasets are sampled from [VOC2012](http://cvlab.postech.ac.kr/~mooyeol/pascal_voc_2012/). Train dataset has 16700 images and Val dataset has 425 images. Download the datasets from [here](https://pan.baidu.com/s/1xuFperu2WiYc5-_QXBemlA)(access code:5tzp), and then extract it into `data` directory. ### Test Image Dataset The test image dataset are sampled from | **Set 5** | [Bevilacqua et al. BMVC 2012](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html) | **Set 14** | [Zeyde et al. LNCS 2010](https://sites.google.com/site/romanzeyde/research-interests) | **BSD 100** | [Martin et al. ICCV 2001](https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/) | **Sun-Hays 80** | [Sun and Hays ICCP 2012](http://cs.brown.edu/~lbsun/SRproj2012/SR_iccp2012.html) | **Urban 100** | [Huang et al. CVPR 2015](https://sites.google.com/site/jbhuang0604/publications/struct_sr). Download the image dataset from [here](https://pan.baidu.com/s/1vGosnyal21wGgVffriL1VQ)(access code:xwhy), and then extract it into `data` directory. ### Test Video Dataset The test video dataset are three trailers. Download the video dataset from [here](https://pan.baidu.com/s/1HB1u-2rkMjX7cVtwNtfWjQ)(access code:956d). ## Usage ### Train ``` python train.py optional arguments: --crop_size training images crop size [default value is 88] --upscale_factor super resolution upscale factor [default value is 4](choices:[2, 4, 8]) --num_epochs train epoch number [default value is 100] ``` The output val super resolution images are on `training_results` directory. ### Test Benchmark Datasets ``` python test_benchmark.py optional arguments: --upscale_factor super resolution upscale factor [default value is 4] --model_name generator model epoch name [default value is netG_epoch_4_100.pth] ``` The output super resolution images are on `benchmark_results` directory. ### Test Single Image ``` python test_image.py optional arguments: --upscale_factor super resolution upscale factor [default value is 4] --test_mode using GPU or CPU [default value is 'GPU'](choices:['GPU', 'CPU']) --image_name test low resolution image name --model_name generator model epoch name [default value is netG_epoch_4_100.pth] ``` The output super resolution image are on the same directory. ### Test Single Video ``` python test_video.py optional arguments: --upscale_factor super resolution upscale factor [default value is 4] --video_name test low resolution video name --model_name generator model epoch name [default value is netG_epoch_4_100.pth] ``` The output super resolution video and compared video are on the same directory. ## Benchmarks **Upscale Factor = 2** Epochs with batch size of 64 takes ~2 minute 30 seconds on a NVIDIA GTX 1080Ti GPU. > Image Results The left is bicubic interpolation image, the middle is high resolution image, and the right is super resolution image(output of the SRGAN). - BSD100_070(PSNR:32.4517; SSIM:0.9191)  - Set14_005(PSNR:26.9171; SSIM:0.9119)  - Set14_013(PSNR:30.8040; SSIM:0.9651)  - Urban100_098(PSNR:24.3765; SSIM:0.7855)  > Video Results The left is bicubic interpolation video, the right is super resolution video(output of the SRGAN). [](https://youtu.be/05vx-vOJOZs) **Upscale Factor
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Yonghye Kwon · MarkAny · South Korea
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:71b60f47f339d2f5, topic:super-resolution, desc:super resolution, readme:super resolution
matched fp:71b60f47f339d2f5, topic:pytorch