Practical insights for a data-driven approach to model optimization
In this last part of my series, I will share what I have learned on selecting a model for image classification and how to fine tune that model. I will also show how you can leverage the model to accelerate your labelling process, and finally how to justify your efforts by generating usage and performance statistics.
In Part 1, I discussed the process of labelling your image data that you use in your image classification project. I showed how define “good” images and create sub-classes. In Part 2, I went over various data sets, beyond the usual train-validation-test sets, with benchmark sets, plus how to handle synthetic data and duplicate images. In Part 3, I explained how to apply different evaluation criteria to a trained model versus a deployed model, and using benchmarks to determine when to deploy a model.
Model selection
So far I have focused a lot of time on labelling and curating the set of images, and also evaluating model performance, which is like putting the cart before the horse. I’m not trying to minimize what it takes to design a massive neural network — this is a very important part of the application you are building. In my…
