Hi,

In our previous posts of the Torch Hub Series, we learned how to use Torch Hub to import models for classification tasks and object detection tasks. Check out our new tutorial by Shivam Chandhok, Torch Hub Series #4: PGAN — Model on GAN. It discusses the Progressive GAN (PGAN) model and provides a step-by-step walkthrough using Torch Hub to import pre-trained generative models and generate high-resolution images.

Image

The big picture: Vanilla GANs, for a long time, have struggled to generate high-resolution images, which has limited their applicability in practical, real-world applications. The PGAN improves on vanilla GANs and effectively generates good-quality images at high resolutions. In addition, as discussed in the previous tutorials of this series, Torch Hub provides an easy and efficient way to integrate these generative models with our deep learning pipelines.

How it works: The PGAN uses a progressive growing paradigm where the GAN is presented with simpler tasks at a time. Then, it gradually learns to capture the high-level and low-level details to generate high-resolution images.

In this tutorial, we discuss in detail:

  • The architectural features that enable the PGAN to generate images at high resolutions
  • How we can use Torch Hub to import a pre-trained generative model like PGAN and seamlessly integrate them with our deep learning pipelines
  • Generate high-resolution face images using our PGAN model and walk-through the latent space of the model

My thoughts: In general, GANs are notoriously difficult to train, and big architecture like PGAN that eventually generates images at resolutions as high as 1024×1024 will require large memory, a handsome amount of GPU resources, and precise execution of training tricks for optimal convergence. Torch Hub reduces this hassle by providing us with an out-of-the-box pre-trained PGAN model, which we can easily load with a single command and start using to generate images at high resolutions.

Yes, but: The models that Torch Hub provides are still limited and trained on a few datasets. Thus, completely depending on Torch Hub for building your deep learning projects is not a great idea. However, the Hub is slowly and gradually developing and is providing weights and support for a diverse range of models for different tasks, as we have seen in other parts of this series.

Stay smart: It is worth noting that in addition to learning how to use Torch Hub models and having them handy in your arsenal, it is also important to understand the architectural details, training paradigms/tricks, and salient features of these models to be able to train them from scratch, in case needed.

Click here to read the full tutorial

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This lesson is part of PyImageSearch University, our flagship program to help you master computer vision, deep learning, and OpenCV.  PyImageSearch University is updated each week with new lessons.

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