Tensorflow trained model speed
I'm a Tensorflow newby and I'm trying to train a 1 class model for object detection. In particular I'm trying to recognize an arrow like the following:
I need a very fast recognition so I started wondering if a pre-trained model can contain such kind of shape. Unfortunately didn't find anything similar and therefor I started with my own training of the arrow using as model the faster_rcnn_inception_v2_coco_2018_01_28.
I'm using his pipeline config, and I'm using his fine_tune_checkpoint as well, is this right considering that I have to train a completely different object?
The result is a training with a very good accuracy but very low speed. I need to increase the framerate and I didn't understand yet if the less is the "training loss" the more is the "object recognition speed", or not.
Any suggestion on how could I speedup the detection?
I'm using his pipeline config, and I'm using his fine_tune_checkpoint as well, is this right considering that I have to train a completely different object?
Yes! Every time you want to change the output of a deep NN, you should take a pretrained model. Training a model from scratch can take several weeks and you will never be able to generate enough data on your own. Taking a pretrained model and fine-tuning it is a way to go.
I didn't understand yet if the less is the "training loss" the more is the "object recognition speed", or not.
No. Training loss just tells you how good your model performs with respect to the training set.
The issue you are having is a classic speed vs. accuracy trade-off. I encourage you to take a look at this table and find a model which is fast enough for you (i.e. lowest run-time) but have decent accuracy. I would first check SSD here.