|
Deci Introduces World's Most Advanced Semantic Segmentation Models
Friday, September 23, 2022
Deci's Deep Learning NAS technology automatically generated a new family of models dubbed DeciSeg, which deliver unparalleled inference performance and accuracy
TEL AVIV, Israel, Sept. 22, 2022 /PRNewswire-PRWeb/ -- Deci, the deep learning company harnessing AI to build AI, today announced a new set of industry-leading semantic segmentation models, dubbed DeciSeg. Deci's proprietary Automated Neural Architecture Construction (AutoNAC) technology automatically generated semantic segmentation models that significantly outperform the most powerful models publicly available, such as the MobileViT released by Apple, and the DeepLab family released by Google. Deci's models deliver more than 2x lower latency, as well as 3-7% higher accuracy.
Semantic segmentation is one of the most widely used computer vision tasks across many business verticals, including automotive, smart cities, healthcare, and consumer applications, and is often required for many edge AI applications. However, significant barriers exist to running semantic segmentation models directly on edge devices, such as high latency and the inability to deploy those models due to their size.
With DeciSeg models, semantic segmentation tasks that previously could not be carried out at the edge because they were too resource intensive are now possible. This allows companies to develop new use cases and applications on edge devices, reduce inference costs (since AI practitioners will no longer need to run these tasks in expensive cloud environments), open new markets, and shorten development times.
"DeciSegs are an example of the power of Deci's AutoNAC engine capabilities to generate custom hardware-aware deep learning models with unparalleled performance on any hardware. AI teams can easily use DeciSegs models or leverage Deci's AutoNAC engine to build and deploy custom models that run real-time computer vision tasks on their edge devices." said Yonatan Geifman, PhD, co-founder and CEO of Deci.
Deci's platform has a proven-track record in enabling AI at the edge and empowering AI teams to build and deploy production grade deep learning models. Earlier this year, Deci announced the discovery of DeciNets for CPUs, which reduced the gap between a model's inference performance on a GPU versus a CPU by half, without sacrificing the model's accuracy, enabling AI to run on lower cost, resource constrained hardware.
"In the world of automated deep neural network design and construction, Deci's AutoNAC technology is a game changer. It uses deep learning to search vast spaces of neural networks for the model most appropriate for a particular task and particular AI chip. In this case, AutoNAC was applied to the Pascal VOC Semantic Segmentation task on NVIDIA's Jetson Xavier NX(TM) chip and we are very pleased with the results." said Ran El-Yaniv, co-founder and Chief Scientist of Deci and Professor of Computer Science at the Technion - Israel Institute of Technology.
Deci's platform is serving customers across industries in various production environments including edge, mobile, data centers and cloud. To learn more about how leading AI teams leverage Deci's platform to build production grade models and accelerate inference performance, visit here.
About Deci
Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company's deep learning development platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment including cloud, edge, and mobile, allowing them to revolutionize industries with innovative products. The platform is powered by Deci's proprietary automated Neural Architecture Construction technology (AutoNAC), which automatically generates and optimizes deep learning models' architecture and allows teams to accelerate inference performance, enable new use cases on limited hardware, shorten development cycles and reduce computing costs. Founded by Yonatan Geifman, Jonathan Elial, and Professor Ran El-Yaniv, Deci's team of deep learning engineers and scientists are dedicated to eliminating production-related bottlenecks across the AI lifecycle.
Media Contact
Garrett Krivicich, Deci, 1 786 233 7684, garrett@headline.media
SOURCE Deci
|
|
|
|
|
|
 |
Energy Toolbase Launches Energy Storage Partnership with Sungrow to Support PowerStack 255CS and PowerTitan 2.0 | Jan 22, 2026
|
|
Einride and IonQ Partnership Uses Quantum Computing to Optimize the Logistics of Electric and Autonomous Freight | Jan 22, 2026
|
|
RS now offers Phoenix Contact's pioneering new NearFi technology | Jan 22, 2026
|
|
SCAILIUM Debuts "AI Production Layer" to Overcome GPU Starvation and Slash AI Energy Waste | Jan 22, 2026
|
|
MetaOptics to Showcase Five Breakthrough Metalens-Powered Products at CES 2026 | Jan 22, 2026
|
|
No Assembly Required: Barrett Distribution Centers Powers Maxwood Furniture's West Coast DTC Expansion | Jan 22, 2026
|
|
Quantum Art Raises $100 Million in Series A Round to Drive Scalable, Multi-Core Quantum Computing | Jan 22, 2026
|
|
TESSAN to Redefine Global Mobility at CES 2026 with '100 Travelers' Initiative and Flagship Voyager 205 | Jan 22, 2026
|
|
Hesai Recognized as the Only Lidar Company on Morgan Stanley's "Humanoid Tech 25" of Global Robotics Leaders | Jan 22, 2026
|
|
1inch Named Exclusive Swap Provider at Launch for Ledger Multisig | Jan 22, 2026
|
|
|
|
