I am new to AI and NN. I've started learning using Geron's book on Tensorflow.
My first project ("Smart Shelf") is to determine which items in a store have been purchased and need refilled. The store camera periodically takes pictures of the tops of items on store shelves. To start, we have only 5 distinct products.
We have created ~250 handwritten images of product-labels that cover these 5 distinct products. So far, the training results are way below our expectation.
I am thinking to augment the training data and see whether it would make any difference.
I have thought about the following strategies:
- Train the model again using grayscale images. https://stackoverflow.com/questions/45320545/impact-of-converting-image-to-grayscale/45321001
- Invert images, translate them horizontally or vertically https://www.tensorflow.org/tutorials/images/data_augmentation, https://nanonets.com/blog/data-augmentation-how-to-use-deep-learning-when-you-have-limited-data-part-2/
Which of the above will yield better results and why? I am curious. Thanks for any help. I feel that I know various data augmentation techniques, but not sure how and why to apply them.
It seems this is a popular question, as learned from Choosing Data Augmentation smartly for different application etc.