Udacity Self-Driving Car students have been writing about the Self Racing Cars track day, the Didi Challenge, and building their own deep learning machines!
Udacity students were sponsored by PolySync to compete in the Self-Racing Cars track day at Thunderhill last weekend, and these photos show what it was like!
Here’s how Tim built his own GPU-enabled deep learning machine. He provides helpful instructions, a bill of materials, links to graphs comparing the value of different NVIDIA GPUs.
“The GPU is the main component of our system, and hopefully comprises a significant fraction of the cost of the system. ServeTheHome has a nice article in which they show the following graph of GPU compute per unit price.”
This is one student’s journal of tackling the Udacity-Didi Challenge. Pay attention to the different neural network architectures he uses!
“Just from these 2 simple steps, I observed the following possible issues:
Small object detection. This is a well-known weakness in the original plain faster rcnn net.
Creation of 2d top view image could be slow. There are quite a number of 3d points needs to be processed
Now that I am sure that the implementation is correct, the next step will be to start training with the actual dataset, which contains many images.”