张量流中任何预训练模型的子集

Question

Can we take subset of any pre-trained model in tensorflow? For example, if we have a pre-trained model which can detect 545 obejcts, can we make a subset of this model which can detect only 20 objects so that the time taken to load the model as well as the detection process can be reduced.

Answer 1

The best you can do is reduce the weights that are related to the last (output) layer only. So, if the size of your second last layer is 1000 so it will reduce your parameters by (1000 * 545 - 1000 * 20) = 525000.
But, if your network is very deep this won't prove to be a great speedup, as you will still need to calculate all the other layers except the last one.

Answer 2

You could, but it's a non-negligible amount of work, and it won't substantially improve the speed.

Indeed, what you'd need to change is only the class prediction layer, which you'd have to reduce from n_featuresx545 to n_featuresx20 . Typically at that stage you have n_features=7*7=49 (although it actually depends on the method you're using; this is true for Faster RCNN with usual settings), so you'd save approximately 26k parameters and 8million operations per image (considering 300 detections per image), which is negligible compared to the millions of parameters and billions of operations usually involved in object detection models.

And changing the prediction layer without retraining and while keeping the trained values is not staight-forward, you'd have to write a piece of code to modify your network manually.