[Tip] Why you need more sources (with examples)

Want to understand the training process better? Got tips for which model to use and when? This is the place for you

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[Tip] Why you need more sources (with examples)

Post by abigflea »

To create a good model you need many sources. Photos and videos.
Typically you will hear you need a minimum of 1000, although more is better and this is to show you why.

Lets say you have a video you want to transfer your friend to.
The original actor would be A ( "A" for Actor).
You have plenty of Actor A. 30 min of video, and 500 good quality pics.

Your friend is "B" , you have very few. As can be expected, this isn't good.

In these example videos I have used increasingly less faces from A for training.
You'll see the quality decline. This will give you an Idea of why you need more data (pics & videos)for a better quality swap.

Bear in mind, I didn't run these models to completion, only enough to show you the decline in quality.
All trained 200K iterations each. Actor A had 6K faces extracted

Actor B with 5400 faces

Actor B with 600 faces

Actor B with 180 faces

Actor B with 78 faces

Actor B with 32 faces

Actor B with 8 faces

As you can see, it gets bad with less data. The inability of the model to convert expressions, head movements. Just gets ugly.

What you should take away from this is MORE DATA for reasonably good results.

:o I dunno what I'm doing :shock:
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Re: [Tip] Why you need more sources (with examples)

Post by HawaiianPizza »

So you need 200,000 iterations to do a half decent job then for a 40 second video? Wow.

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Re: [Tip] Why you need more sources (with examples)

Post by bryanlyon »

The length of the video doesn't matter at all. You're training on faces, not a video. A short video doesn't take any less time than a long one since you'll have to train a model with sufficient data before you can use it to convert.

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Re: [Tip] Why you need more sources (with examples)

Post by d3x »

HawaiianPizza wrote: Sun Dec 04, 2022 11:17 am

So you need 200,000 iterations to do a half decent job then for a 40 second video? Wow.

200K iterations is a totally arbitrary number, it can take a very short or very long time to get there depending on many different factors such as model, learning rate, loss functions & batch size

It's just a simple example to demonstrate the effects of having too little training data for your B dataset

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