I'm currently trying to build a model to recognize approximately 10 labels (food items) in a fairly controlled environment (refrigerator). I was unable to find datasets that worked well enough for my task, so I'm attempting to curate one myself.
So far this has been my approach:
- Capture images with a smartphone
- Annotate in LabelImg
- Train in Detectron2 / pyTorch
I have 2 questions:
Given that the environment is somewhat consistent throughout all samples, is there a ballpark number / rule of thumb for a decent number of samples to use per class? (100 train / 100 test)? This is a proof of concept project, so I'm just looking for something with reasonable accuracy (80%+)
After I've captured my images (let's say via smartphone) are there any preprocessing steps (other than annotating) that are necessary before using as training data? (i.e resize the images, reduce file size, format)
Absolutely any help is appreciated, and of course other tips/suggestions you feel may be useful.