Answers.Body: <blockquote>
    <p>With a Google Cloud V100 GPU the GAN would run a week to two with
    default parameters. Does this sound realistic time for this kind of
    dataset? It's definitely not feasible for me.</p>
  </blockquote>
  
  <p>Yes, V100s are quite beefy. You shouldn't even need a week. Obviously this is based on my experience with various problems, rather than a concrete calculation.</p>
  
  <blockquote>
    <p>Is 4000 images enough to train a GAN from scratch?</p>
  </blockquote>
  
  <p>For the size you want to generate, it is still on the edge of what would constitute a decent training set. You will get some results(depending on architecture) but will probably want to grab more data if at all possible.</p>
  
  <blockquote>
    <p>Is there any implementation with pytorch/keras that would be good to
    get nice results with?</p>
  </blockquote>
  
  <p>I would check out this link: <a href="https://github.com/eriklindernoren/Keras-GAN" rel="nofollow noreferrer">https://github.com/eriklindernoren/Keras-GAN</a>. It has some nice implementations in Keras. As far as the particular GAN to use, I would start out with a vanilla GAN that fits your purposes and focus on toying with hyperparameters, and if that doesn't work, look into one of the other variations that correlate well with your problem.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-02T16:01:08.697Z
Answers.LastActivityDate: 2019-04-02T16:01:08.697Z
Answers.UserDisplayName: hisairnessag3
Body: <p>I'm trying to generate images at minimum of size 128 x 128 with a Generative Adversarial Network. I already tried a SAGAN pytorch implementation, but I'm not very happy with results. The images look cool but and I see some correct shape but without explanation you wouldn't know what the images are about. I have a dataset of 4000 images. Lightness, colors and shapes vary a lot, but they are similar in style and on what they portray. </p>
  
  <ul>
  <li>With a Google Cloud V100 GPU the GAN would run a week to two with default parameters. Does this sound realistic time for this kind of dataset? It's definitely not feasible for me.</li>
  <li>Is 4000 images enough to train a GAN from scratch?</li>
  <li>Is there any implementation with pytorch/keras that would be good to get nice results with?</li>
  </ul>
  
Tags: <generative-model><generative-adversarial-networks>
Title: How to get good results with GAN and some thousands of images?
UserDisplayName: tjk
ThreadText: # How to get good results with GAN and some thousands of images?
  
  (Asked by: username_0 on 2019-04-02)
  
  I'm trying to generate images at minimum of size 128 x 128 with a Generative Adversarial Network. I already tried a SAGAN pytorch implementation, but I'm not very happy with results. The images look cool but and I see some correct shape but without explanation you wouldn't know what the images are about. I have a dataset of 4000 images. Lightness, colors and shapes vary a lot, but they are similar in style and on what they portray.
  
  * With a Google Cloud V100 GPU the GAN would run a week to two with default parameters. Does this sound realistic time for this kind of dataset? It's definitely not feasible for me.
  * Is 4000 images enough to train a GAN from scratch?
  * Is there any implementation with pytorch/keras that would be good to get nice results with?
  
  **Tags:** generative-model, generative-adversarial-networks
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-04-02. Score: 1 | Accepted answer
  
  > With a Google Cloud V100 GPU the GAN would run a week to two with
  > default parameters. Does this sound realistic time for this kind of
  > dataset? It's definitely not feasible for me.
  
  Yes, V100s are quite beefy. You shouldn't even need a week. Obviously this is based on my experience with various problems, rather than a concrete calculation.
  
  > Is 4000 images enough to train a GAN from scratch?
  
  For the size you want to generate, it is still on the edge of what would constitute a decent training set. You will get some results(depending on architecture) but will probably want to grab more data if at all possible.
  
  > Is there any implementation with pytorch/keras that would be good to
  > get nice results with?
  
  I would check out this link: <https://github.com/eriklindernoren/Keras-GAN>. It has some nice implementations in Keras. As far as the particular GAN to use, I would start out with a vanilla GAN that fits your purposes and focus on toying with hyperparameters, and if that doesn't work, look into one of the other variations that correlate well with your problem.
  
  
  ---
  
  