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Google Announces New AutoML Vision Edge & AutoML Video Upgrades
Google Announces New AutoML Vision Edge & AutoML Video Upgrades

Google Announces New AutoML Vision Edge & AutoML Video Upgrades

Published Updated

Key takeaways

  • Google has introduced enhancements to its Vision AI portfolio, aimed at leveraging artificial intelligence for various business applications.
  • AutoML Vision Edge now includes object detection capabilities alongside image classification on edge devices.
  • The new features of AutoML Vision Edge are optimised for low latency and high accuracy across multiple hardware platforms.
  • AutoML Video has also been upgraded to track the movement of multiple objects between frames, enhancing video analysis.
  • These advancements enable businesses to improve processes such as retail inventory management and predictive maintenance.
5 min
google Whether businesses are using machine learning to perform predictive maintenance or create better retail shopping experiences, machine learning has the power to unlock value across a myriad of use cases. Now this week, Google introduced a number of enhancements to their Vision AI portfolio to help even more customers take advantage of artificial intelligence. One of the biggest enhancements is the fact that AutoML Vision Edge now detects objects. It can now perform object detection as well as image classification—all directly on the edge device. Object detection is critical for use cases such as identifying pieces of an outfit in a shopping app, detecting defects on a fast-moving conveyor belt, or assessing inventory on a retail shelf. AutoML Vision Edge models are optimized to a small memory footprint and offer low latency while delivering high accuracy. AutoML Vision Edge supports a variety of hardware devices that use NVIDIA, ARM, or other chipsets, as well as Android and iOS operating systems. TRYONs_AutoML_Vision_Edge_powered_AR_shopping_experience   Google has also brought object detection to AutoML Video, enabling it to track the movement of multiple objects between frames. This is an important component of a broad range of applications such as traffic management, sports analytics, robotic navigation, and more. Their Video Intelligence API can now recognise logos, too. It basically used to offer pre-trained machine learning models that automatically recognise a vast number of objects, scenes, and actions in stored and streaming video. Now, the Video Intelligence API can also detect, track and recognize logos of popular businesses and organizations. With the ability to recognize over 100,000 logos, the Video Intelligence Logo Recognition feature is ideal for brand safety, ad placement, and sports sponsorship use cases.
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