My collection of posts related to machine learning.
I was recently selected to participate as a student leader during my PyTorch Facebook Challenge for the student mentor channel. The main purpose of this group is to pair students with a mentor that can advise them when necessary. One of the most time consuming part is to match the students and mentors since everyone has a different level skillset and speak different languages.
One of the vital portion of a self-driving car is to be able to detect other cars around it. Typically, computer vision is used to detect a car and sensor fusion with lidar/radar is used to locate the cars within the vicinity. This write-up will mainly focus on how to detect a car using computer vision. Also, one of the challenging task is to be able to detect cars quickly with minimal hardware since all the inference are done on-board.
Transfer learning is a good method to use when you either have a small dataset and/or the features you are looking to classify is similar to the existing pretrained models. One of the most famous single image, multiobject classfier is YOLO created by PJReddie (John Redmon). By transfer learning with the preexisting weights provided by PJReddie, you can achieve a model with an extremely high IOU by using minimal hardware or training time (about 1 hour on a Tesla K80). However, transfer learning with YOLO can be convoluted since it uses PJReddie’s self written neural network library called darknet.