Month: February 2021

This week I have been working pretty much exclusively on research and my other capstone. Although I basically finished the other capstone already, as it has accomplished all tasks that have been set out for it , I have continued to improve on it. I home brewed a small YOLO (you only look once) CNN for multi-object detection. It’s much more complex than a simple resnet implementation as it requires a custom loss as opposed to a simple MSE loss method. I managed to get it to work, and it gave me a lot of insight in how to create a functioning NN that is specifically crafted for a specific scenario.

Now, moving forward, I will be using a lot of these new ideas in implemented a new NN for the main capstone. However, just three hours ago, my power supply for my laptop blew out, which means that priorities have shifted a bit. I am currently staying in my parents house in Portland, and don’t have access to my backups on my desktop. So now it’s a game of trying to get files over to this temporary laptop (my moms) to get something working. Hopefully it will all work out and I’ll be able to get some of my new code out for the code review on Sunday.

Now that our group is nearing the end of our project, we can begin to mess around with different architectures that may help in categorizing different gestures. I have been researching residual networks, which may help with the disheartening gradients issue. I do not know how it will interactive with a 3D CNN/LSTM combination but we shall see.

I have gotten more and more frustrated with attempting to get a new GPU for training neural nets. Currently the market for GPUs is barren as there are tariffs on imports and crypto currency is exploding again. Many people are attempting to enter into this market and are now buying up the brand new GPUs. Along with this, scalpers have taken advantage of the limited supply and are charging exhobinant amounts (3-4x MSRP). I even asked my mentor if they had an in on GPU’s and apparently Intel is having trouble acquiring them also.

This week my team and I have been working on consolidating many different aspects of the project together. This involves talking with our other team members to get the gui setup, along with using OpenVINO to make the neural net more effective. As we are nearing the end of the capstone project, it makes sense to start winding down on a lot of the features, and start working on making what we currently have done. So far, due to how the nature of creating neural nets is so tedious and also hard to determine, we don’t have a neural net that actually functions to specifications. So It is a good idea to really hammer that down before we move onto anything else.

On other news, we finally have a method of mass generating figures for a neural net to train on, which will help increase accuracy and speed on up figure generation immensely.

I’ve been lazing around quite a bit this week when it comes to the capstone project. It’s midterms week and I’ve been focusing on studying and finishing assignments for my other classes. That’s not to say that I have not been working on the capstone at all, it’s just that I have not made as much progress that I would have liked to have made. I’ve been working on a label creator that would take very few inputs of information, mutate them into several different images a NN would still recognize and then stitch them together. This would allow for a small batch of 26 gestures to be expanded into 100k+ data sets.

I’ve also finally got the last parts for my second capstone. Nothing too special, just a couple of leds and some resistors that will determine what state the camera is in, ae: still, moving, and not registering anything. With this, I can submit this second capstone and move forward and spend more time on other assignments.

This week I have mostly been working on my second capstone project. This is for my second major, as I need to have some aspect of ECE in my second project so I can have it count for both majors. In my second project, I am designing a convolutional neural network to locate the users hand and follow it. The camera taking the photos of the user will be mounted on a servo which are both hooked to a raspberry pi. This way the raspberry pi can communicate with the camera, asses the network and then move the servo with the camera attached to follow the users movement. So fat, as of this post, I have finished most of these tasks. However, a lot of the current setup has errors in it. The current CNN has a high misidentification rate if the background has changes, and will sometimes target the users head instead. The servo will also jump back and forth of the the object when it has reached center frame.

I have also managed to get the LSTM NN working properly. It will recognize a gesture only when it has followed the proper movement, and not when it is a still image. However, this also has identification. General movements taht follow the same arc as the gesture will also be identified as it.