Optimizing Neural Network for Computer Vision task in Edge Device

With the rise of Artificial intelligence, it is realized that deep-learning-based approaches give satisfactory results compared to numerous other state-of-the-art schemes which are hand-engineered to all the computer vision tasks. The features learned from Convolutional neural networks outperform other hand-engineered feature-based methods like SIFT [Mikolajczyk and Schmid (2004)] and HoG [Dalal and Triggs (2005)] in computer vision tasks like image classification and object detection. The availability of large datasets and powerful computation devices made it possible to train the large and complex neural networks to obtain the desired performance on many computer vision tasks. The large amount of open-source pre-trained models trained on large datasets like ImageNet [Challenge], MS-COCO [Lin et al. (2014)], SHVN [Netzer et al. (2011)] created a large number of useful filters especially the features learned from the initial layer helps in transfer learning a lot. In transfer learning, most of the time only the last few layers of pre-trained models are modified and trained which counters the problem of having fewer data to a certain extent. Currently, a variety of embedded systems are deployed but the usage of neural networks is limited in edge devices like microcontrollers, Raspberry Pi. Household devices like Refrigerators, washing machines use a set of logic, rules for their automatic operations. By optimizing the network trained on a dataset traditional way of controlling can be replaced by intelligently monitoring the systems with the power of AI and neural networks [Ranjith M S and Parameshwara (2020)]. Optimizing the convolutional neural network architectures like ResNet [ He et al. (2016)], DenseNet [ Huang et al. (2017)], AlexNet [ Krizhevsky et al. (2012)] which are generally used in computer vision tasks allows creating many useful applications.