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The example dataset should have at least one instance of each type of issue that VideoLab can detect, but should not be large (so that user's can fetch it and run videolab quickly). Please provide a link that shows others have the right to distribute this dataset.
The first step of the notebook should be to unzip a zipped folder containing all the videos.
You can share this folder with our team who will make it available in S3 and update the tutorial as needed to load it from there.
The text was updated successfully, but these errors were encountered:
Yep this looks great so far! It would be super helpful if you're also able to add in just one short video with each of these issues:
['grayscale',
'odd_size',
'odd_aspect_ratio']
As you mentioned it might be easiest to create videos that have these issues yourself, or you can just link to short clips that exhibit these properties you found online.
Goal: create a tutorial notebook that looks like this one: https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html
But runs VideoLab instead on an example video dataset.
The tutorial should be a notebook PR'd to here:
https://github.com/cleanlab/cleanvision/tree/main/docs/source/tutorials
(this new notebook should look just like the other tutorials)
The example dataset should have at least one instance of each type of issue that VideoLab can detect, but should not be large (so that user's can fetch it and run videolab quickly). Please provide a link that shows others have the right to distribute this dataset.
The first step of the notebook should be to unzip a zipped folder containing all the videos.
You can share this folder with our team who will make it available in S3 and update the tutorial as needed to load it from there.
The text was updated successfully, but these errors were encountered: