Hey, So when I started thes course I had a project goal in mind. I’m trying to write a bot that plays video games. The actual control code will be hand written because the goal of the project is to iterate on strategies and stuff (not to do a full reinforcement learning game playing bot) but I want to use deep learning for the up front scene recognition.
Given a frame of the game (or more likely the last few frames of the game) I want to know what sprites are on screen, where they are and what direction and speed they are traveling at.
From what I’ve learned so far I think solving the problem is going to look like this.
- Take some existing object recognition model (fast R-CNN looks like it might be a starting point) and finetune it to recognize the sprites in my game.
Is this the right approach?
Also once I have a model trained on my data set I really need it to execute FAST. (like 60fps if possible) Can you take a model trained in keras and “export” the weights and the architecture so that I could write some c code that just evaluates the model on new data as fast as possible? Is that a typical thing to do?