Imagenet 2014 competition is one of the largest and the most challenging computer vision challenge. This challenge is held annually and each year it attracts top machine learning and computer vision researchers. Neural networks, specifically convolutional neural networks again made a big impact on the result of this year’s challenge . Google’s approach won the classification and object recognition challenges. Google used a new variant of convolutional neural network called “Inception” for classification, and for detection the R-CNN  was used. The results and the approach that Google’s team took are summarized here [2, 3]. Google’s team was able to train a much smaller neural network and obtained much better results compared to results obtained with convolutional neural networks in the previous year’s challenges. Andrej Karpathy, one of the organizer of the competition, summarized his experience and the challenge itself in his blog post .
 Imagenet 2014 LSVRC results, http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/, Last retrieved on: 19-09-2014.
 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Going Deeper with Convolutions, Arxiv Link: http://arxiv.org/abs/1409.4842.
 GoogLeNet presentation, http://image-net.org/challenges/LSVRC/2014/slides/GoogLeNet.pptx, Last retrieved on: 19-09.2014..
 What I learned from competing against a convnet on imagenet, http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/, Last retrieved on: 19-09-2014.
 Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” arXiv preprint arXiv:1311.2524 (2013).