<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>

CC BY-SA 4.0
2019-04-02T16:01:08.697Z
2019-04-02T16:01:08.697Z
hisairnessag3
<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>

<generative-model><generative-adversarial-networks>
How to get good results with GAN and some thousands of images?
tjk
# 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


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## 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.


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