A few initial thoughts:
For expanding data sample why not simply use modern books or bronze age? If you limit the initial model training to specific atomic characteristics, they should remain consistent across the sample ( spine tics, % of cover missing, color breaks, etc)
Why try to create a grade initially? It seems like a report of the characteristics is the first stage or goal for generation. The model or a clustering model, could be used later to try to create a grade but initially, before even hammering out core metrics, it's adding confusion in the thread and another level of complication (ie failure).
This can be done. Insurance companies use ML modeling on car claims from adjusters to make initial projections for returns. Travel companies use it to make recommendations on hundreds of thousands of photos (air bnb, booking, expedia, etc). There's a ton more examples but for identifying and measuring set parameters, it can be done.
For standardization of photo quality, we're this to ever be more then a fun excercise, it would likely need to be a mail in service or have some parameters around resolution, lighting, etc. However, I'd consider all of that a later problem for new users. Yes there would have to be some data cleaning for photos contributing to model training but OP already discussed some of that.
Tensor Flow and the Google Cloud suite is a great place to start though I'm biased.
Happy to discuss more here or elsewhere too! Ive been meaning to try this for a while too so glad to see there's dozens of us :-)
A few initial thoughts: For expanding data sample why not simply use modern books or bronze age? If you limit the initial model training to specific atomic characteristics, they should remain consistent across the sample ( spine tics, % of cover missing, color breaks, etc) Why try to create a grade initially? It seems like a report of the characteristics is the first stage or goal for generation. The model or a clustering model, could be used later to try to create a grade but initially, before even hammering out core metrics, it's adding confusion in the thread and another level of complication (ie failure). This can be done. Insurance companies use ML modeling on car claims from adjusters to make initial projections for returns. Travel companies use it to make recommendations on hundreds of thousands of photos (air bnb, booking, expedia, etc). There's a ton more examples but for identifying and measuring set parameters, it can be done. For standardization of photo quality, we're this to ever be more then a fun excercise, it would likely need to be a mail in service or have some parameters around resolution, lighting, etc. However, I'd consider all of that a later problem for new users. Yes there would have to be some data cleaning for photos contributing to model training but OP already discussed some of that. Tensor Flow and the Google Cloud suite is a great place to start though I'm biased. Happy to discuss more here or elsewhere too! Ive been meaning to try this for a while too so glad to see there's dozens of us :-)
