Are There Any Resource Efficient Image Recognition Systems?
Each working with neural networks and notably machine vision systems in business environment knows firsthand how resource-intensive they could be. Almost all the projects struggle with transitive data storing, computational power shortages, scalability limitations, etc.
Most often, computer vision algorithms and applications are designed to recognize a limited number of features, for example, faces only, to tackle the issue. This usually leads to limiting business opportunities. In turn, the reducing of convolutional layers number negatively influences the recognition results. Even parallelizing the processes isn’t a silver bullet as it lowers the accuracy of generated features.
Striking a balance between the high functionality and rational resource consumption — that’s occasionally a dead lift for amateur machine vision companies. Only a highly expertised team is able to cope with the task.
At ProVision Lab we use the most efficient optimization methods. For instance, they include objectness estimation to exclude spare image space from computations and great memory optimization techniques enabling the use of 2–3 times less memory.
Entrust complex tasks to professionals only. Contact us today to get more details and stop limiting your opportunities!
