Let's suppose I would like to classify motorbikes by model.
- There are couple of hundreds models of motorbikes I'm interested in.
- I have hundreds of labelled pictures for each motorbike
- There are also unlabelled set of motorbikes pictures, it's about 100x times bigger than the labelled set.
Can you please point me to the practical example that demonstrates how to train model on your data and then use it to classify images? It needs to be a deep learning model, not simple logistic regression.
I'm not sure about it, but it seems like I can't use pre-trained neural net because it has been trained on wide range of objects like cat, human, cars etc. They may be not too good at distinguishing the motorbike nuances I'm interested in.
I found couple of such examples (tensorflow has one), but sadly, all of them were using pre-trained model. None of it had example how to train it on your own dataset.