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Here's something I've found really useful in my DeOldify research: Keep a separate huge master set of images created from various sources (eg open images), then use Jupyter notebooks made specifically to generate training datasets from that master and output them elsewhere. 1/
In the notebooks I do a lot of filtering out of images from the master, depending on the task. You can filter out images for being grayscale when you're doing colorization, for example. Or filtering out for a min resolution, etc. I've found this helps data quality a lot. 2/
The bigger your master set is, the more aggressive filters you can apply and still get a big enough dataset at the end. This also gives you the freedom to use sloppy but good enough filtering techniques that have a lot of false positives such as blurry image detection. 3/
For the master you can use a huge and fairly slow/cheap drive as opposed to a fancy ssd/nvme. Save the speedy drive space for the resulting filtered and processed datasets. 4/
Hi Jason, thank you for creating Deoldify, Could you create (please) a video explaining step by step how to install and use Deoldify on our own computer in Ubuntu and with videos hosted on our computer? I am a newbie and I can't get Deoldify to work.
Or use the deoldify GoogleColab notebook. Select GPU. Click "run all". Done
Thanks for sharing! Your work is inspiring. I used the free Deoldify recently, which made somebody's day 
. How do you store the metadata for filtering?
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