Barker, J. and Prasanna, S. (2016) Deep Learning for Object Detection with DIGITS.
https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

has been cited by the following article:

• TITLE: Classifying 3 Moss Species by Deep Learning, Using the “Chopped Picture” Method

  AUTHORS: Takeshi Ise, Mari Minagawa, Masanori Onishi

  KEYWORDS: Remote Sensing, Classification, Deep Learning, Object Identification

  JOURNAL NAME: Open Journal of Ecology, Vol.8 No.3, March 20, 2018

  ABSTRACT: Especially in recent years, deep learning has become a very effective tool for object identification. However, in general, the automatic object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. The technique called chopped picture method, where teacher images are systematically dissected into numerous small squares. As a result, the model correctly classified 3 moss species and “non-moss” objects in test images with accuracy more than 90%. Using this approach will help progress in computer vision studies for various ambiguous objects.