I am new to the field of AI but due to the high level of abstraction that comes with services such as Google VisionAI I got motivated to write an application that detects symbols in photos based on tensorflow.js and a custom model trained in Google Vision AI.
My App is about identifying symbols in photos, very similar to traffic signs or logo detection. Now I wonder if
- I should train the model based on real, distorted and complex photos that contain those symbols and lots of background noise
- if it was enough to train the model based on cropped, clean symbols
- A hybrid of both
I started with option a and it works fine, however it was a lot of work to create the training dataset. Does the model need the distorted background to work?