Training SRGAN to Generate High-Resolution Images

Explore training SRGAN to generate high-resolution images by optimizing GPU usage and applying data prefetching methods. Understand how SRGAN improves image sharpness and learn to evaluate model progress during training.

We'll cover the following...

GPU consumption by data
Model evaluation

Of course, we need to have some data to work with.

⚠️ The dataset is intended only for non-commercial research and educational use.

The image collection we will be using is the VOC2012 datasethttps://www.kaggle.com/datasets/gopalbhattrai/pascal-voc-2012-dataset. With a batch size of 64, it takes several hours to train for 100 epochs on a GTX 1080Ti graphics card. GPU memory usage increases as we increase batch size.

Create a folder named data and place the training images into a folder called ...

1.Getting Started

2.Generative Adversarial Networks Fundamentals

3.Best Practices for Model Design and Training

4.Building Our First GAN with PyTorch

5.Generating Images Based on Label Information

6.Image-to-Image Translation and Its Applications

7.Image Restoration with GANs

8.Training GANs to Break Different Models

9.Image Generation from Description Text

10.Sequence Synthesis with GANs

11.Reconstructing 3D Models with GANs

12.Concluding Remarks

13.Appendix

Training SRGAN to Generate High-Resolution Images