Date: Tuesday, September 22, 2020
Start Time: 9:30 am
End Time: 10:30 am
Data augmentation is a method of expanding deep learning training datasets by making various automated modifications to existing images in the dataset. The resulting increased data diversity can enable a more accurate and robust model without the need to manually obtain more images. In this presentation, we explore practical methods of image data augmentation for training object detection models. We will also show how to create an augmented dataset of 50,000 unique images with labeled bounding boxes in a few hours using a short Python script.
