This week, I tested the pipeline for the machine learning model using some pictures taken from my phone. Until the real data is available, the model cannot be trained. However, in the meanwhile we can ensure that when the data is available, the code is able to run smoothly.
When I had last created the pipeline, I did not have any data to run the code on. However, this week when I used the images I took from my phone, it revealed some bugs. None of the bugs were individually worth mentioning, but I spent a large amount of time debugging. Once the dataset from the cameras is available, we should be able to run them through the model and start with transfer learning.
I also read up on web sockets. Last week, I spoke to Arjun about how the server would communicate with the central node. We had two options, post requests and web sockets. Post requests come with problems regarding the CSRF tokens. On the other hand, I personally did not have a lot of knowledge about sockets. This week, after reading up on sockets and Django Channels, I can implement web sockets for communication with the central node. This will be faster than a post request, since the socket is constantly connected, and so data can be sent over faster. We still have the option of implementing Post requests, but as of now I will experiment with making a web socket connection.