Beamr Imaging release results of a case study on tech enabling machine learning

Beamr Imaging has released the results of a case study which highlights how Beamr tech enables accelerated machine learning training by using significantly smaller video files and without any negative impact on the video artificial intelligence process. Machine learning for video is becoming an increasingly significant technology for businesses. But the players in this expanding arena face critical pain points, like storage and bandwidth bottlenecks or the difficulty to reach acceptable training and inferencing speeds. In this case study, Beamr’s R&D team showed that training an AI network using video files compressed and optimized through Beamr’s Content-Adaptive Bitrate technology produced results that are as good as training the network with the original, larger files. The AI network was trained to fulfill the task of action recognition, such as distinguishing between people who are walking, running, dancing or doing many other day-to-day actions. The video files used for machine learning training were optimized by Beamr Cloud, reducing file sizes by 24%-67%. Such a reduction is beneficial when storing video files for future use and possibly performing other manipulations on them. Recently-launched Beamr cloud is an optimization and modernization software-as-a-service hat enables automated, efficient and fast video processing, through no-code processes or customized pipelines to meet specific user needs. Training performed with the smaller video files optimized by Beamr tech, provided results which were equivalent to those obtained with the larger and non-optimized files. The case study is part of Beamr’s ongoing commitment to accelerate adoption and increase accessibility of machine learning for video and video analysis solutions. A previous case study focused on the AI network inference stage, which is the phase of drawing conclusions from an AI network that has already been trained. The previous experiment found that video files that were downsized by 40% on average streamlined machine learning processes. This allowed significant savings in storage and costs. The current case study covers the more challenging task of training a neural network for action recognition in video. In coming months, the Beamr R&D team plans to expand the initial experiment described above to large scale testing, including neural networks that operate in the cloud using GPUs.