MobileNet Pre-Trained Model - Classification

Question

I am currently working with a pre-trained MobileNet model that classifies images from a set of 1000 categories. For the purpose of my IOS application, I only need it to recognize/classify one type of object in the scene. How can I train the model so that it only classifies the one object I need but does it extremely well?

I am new to machine learning and unfamiliar with transfer learning techniques. Would doing this type of training reduce the model size and make it more efficient at recognizing the one object I need? If yes, what are resources that teach me how to keep training this pre-trained model for my objective.

Answer 1

Briefly, you want to turn your 1000-way classifier to a binary classifier. The answer below assumes you have access to the original data, and that you know how to train the original model (that is, you have access to the training script). Here goes:

Assuming you're only interested in a single category C, you want to first map all instances (x, C) of the data to (x, 1) and all other instances (x, not_C) to (x, 0), then train a model on the resulting data (or, continue training the pre-trained model, if the training script also accepts a starting point for the model).

The model would then lose the ability to discern between non-C classes, and hopefully become better at discriminating C vs non-C instances.

Note: A less hacky approach would be to actually restrict the model to output only 0 or 1 and change the objective to a binary softmax. However, that would require some manipulation of the model's architecture, which you can do without.