Recent progress in deep learning methods has shown that key steps in object detection and recognition, including feature extraction, region proposals, and classification, can be done using Convolutional Neural Networks (CNN) with high accuracy. However, the use of CNNs for object detection and recognition has significant technical challenges that still need to be addressed. One of the most daunting problems is the very large number of training images required for each class/label. One way to address this problem is through the use of data augmentation methods where linear and nonlinear transforms are done on the training data to create "new" training images. Typical transformations include spatial flipping, warping and other deformations. An important concept of data augmentation is that the deformations applied to the labeled training images do not change the semantic meaning of the classes/labels. In this paper we investigate several approaches to data augmentation. First, several data augmentation techniques are used to increase the size of the training dataset. Then, a Faster R-CNN is trained with the augmented dataset for detect and recognize objects. Our work is focused on two different scenarios: detecting objects in the wild (i.e. commercial logos) and detecting objects captured using a camera mounted on a computer system (i.e. toy animals).