Retraining of object detection models I came across the concept of retraining of models a few days back.
So tensorflow recommends to retrain an already trained model instead of making a new one from scratch so that the whole process takes less time. But I can't understand how this is going to work.
Like if I take a pretrained model (trained to classify cats and dogs), how can I use its parameters to train a whole new model that I want to make (to classify different flowers).
It will not
It's about using a model which was trained on thousands of Classes on Millions of images of ImageNet.
 Chances are very high that most of the classes you have in your dataset is already there.
In general, if you trained a model on a super-class (e.g. vehicle), then you may reuse it to classify the Car variant(Utilizing its initial layers).
Point is to reuse the characteristics already learned by another model. It can reduce training time drastically.