Developing a Real-life DNN-based Embedded Vision Product
for Agriculture, Construction, Medical, or Retail.
What it takes to succeed in a real-life development of a DNN-based embedded vision product? You have your hardware and software building blocks – want’s next? Learn how to plan and design for deep learning, how to select and cascade algorithms, where to get the training data and how much is enough, and how to optimize and troubleshoot your product.
By now we very well know how to design and train a neural network to recognize cats, dogs and cars. But what about real projects — agriculture, construction, medical, retail? This how-to talk will provide an overview of what it takes to design, train, and fine-tune a real-life DNN-based embedded vision solution. Presentation will explore algorithmic, data set, training, and optimization decisions that take you from proofs-of-concepts to solid, reliable, and highly optimized systems. This material is based on our own successes, failures, and other lessons we learnt while implementing embedded vision solutions over the past few years.
Alexey Rybakov is Senior Director with Luxoft, and manages software R&D, consulting and optimization services in artificial intelligence, deep learning, computer vision, and video processing.