This week, I had to make a major revision on my codebase, since in the previous implementation, I completely forgot that we had to deploy the model to read in data in real-time and rather had the model read in images for data. This meant that I had to change the model to extract features and read data in real-time video processing instead of on static image data.
I’m a week behind in terms of what I had to achieve– I had to deploy the model on raspberry pi. However, the issue stems from delivery issues, so this was quite inevitable. As such, to mitigate this, I will have to devote some good amount of time once we get the SD card and make 2 weeks amount of effort.
Now, the model is designed to work on real-time streamed video. However, since we haven’t been able to gather training data for the cards, I was not able to test the model. The next step I’m thinking is maybe I could train and evaluate the model on dummy data so that we can see if the basic object detection is working. Subsequently, once I’m able to gather the training data, my plan is to train and evaluate the model to detect and classify the cards that can be deployed in the dealer system.