Deep Learning
Fei-Fei Li wasn’t thinking about either neural networks or GPUs as she began a new job as a computer science professor at Princeton in January of 2007. While earning her PhD at Caltech, she had built a dataset called Caltech 101 that had 9,000 images across 101 categories.
That experience had taught her that computer vision algorithms tended to perform better with larger and more diverse training datasets. Not only had Li found her own algorithms performed better when trained on Caltech 101, other researchers started training their models using Li’s dataset and comparing their performance to one another. This turned Caltech 101 into a benchmark for the field of computer vision.
