Artificial Intelligence

Instance Segmentation [1] is one of the hottest areas for research in the field of Artificial Intelligence . The process in its present form relatively computationally expensive, therefore the present architectures that are able to generate the best results are not very suitable for “real-time” applications. The networks that yield good results in instance segmentation are FCIS [2] , Mask-R CNN [3] , RetinaMask [4] , PA-Net [5] etc. These frameworks although perform relatively well but the inference obtained from them can’t be used in “real-time” due to the computational complexity that is involved in the creation of such systems, the sheer number of parameters makes it impossible for these network to perform on machines with lower computational capability. The task therefore requires a different architecture that is able to perform in the “real time” computations. YOLACT to the rescue YOLACT [6] (You only look at the coefficients) is a more optimized version for instance segmentati

With the advancement in modern technologies, Artificial Intelligence (AI) has made its presence felt in the market. Object detection technology is a hot topic in today’s scenario. Most big companies are making great use of face detection, still image object counting, amongst others. We all know how efficiently computer vision object detection models run on desktop and cloud services. However, in some cases these AI models would require small size devices or hardware for a mobile user. It would also provide the user with the much sort-after aspect of privacy and this is the reason why TensorFlow Lite (TF Lite) came into existence. Detecting Living and Non- Living Objects What Exactly is TensorFlow Lite? TensorFlow Lite is a technology specially designed for mobile phones and smart devices by TensorFlow. As the name suggests, 'Lite' stands for lightweight. It is highly advantageous when looking at the latest technological scenario. TensorFlow allows running machine-learned mod

Lately, there has been a lot of talk regarding the possibility of machines learning to do what human beings do in factories, homes, and offices. With the advancement in artificial intelligence , there has been widespread fear and excitement about what AI, machine learning, and deep learning is capable of doing. What is really cool is that Deep Learning and AI models are making their way from the cloud and bulky desktops to smaller and lower powered hardware. In this article, we will help you understand the strengths and weaknesses about three of the most dominant deep learning AI hardware platforms out there. The Intel Movidius Neural Compute Stick (NCS) with Raspberry Pi Developed by Intel Corporation, the Movidius Neural Compute Stick can efficiently operate without an active internet connection. Its computing capabilities come from the Myriad 2 Vision Processing Unit (VPU) . It offers profiling, tuning, and compiling a Deep Neural Network (DNN) on a development computer with the ri