How would you most likely create a large production ready image training dataset from scratch including annotations for a image classification task? We will take a large amount of images (~1 million) with industrial cameras and save them in a S3 bucket. Do you think a data lake infrastructure is necessary?
In your opinion, what are the most suitable methods for annotating the images in the shortest possible time (bounding boxes not needed). Solutions that I have been able to find so far are the following:
- Use an open source web based image annotation tool like make-sense or LOST (Problem: who will annotate the images? These tools doesn't seem to be perfect for big amount of image data). See also awesome-data-labeling
- Build a gamificated web application and let users annotate images and earn discount codes to motivate them
- Use third party tools with annotation workforces like Playment, Labelbox, Amazon Mechanical Turk
Are there any options I missed? In principle, it would be possible to pay for the annotation, but should be avoided or kept as small as possible.
Are there things that should be considered architecturally with such a large database?