I would like to train a tensorflow model to recognize a very specific home appliance. I'm familiar with using Tensorflow to do a variety of things, but I'm unsure of the optimal way to build the dataset.
My initial thought is to supply myself with 100-200 images of the appliance from multiple angles, and then 1000 or so images of other appliances from google search that are not correct. Is there a better way than how I plan to tackle it?
I am ok with the recognition being much better from sides of the object that container more characteristics, but hope to be able to attain a high level of accuracy from the front angles.