AI-powered video analytics development and deployment effort.
Watch a compilation of clips demonstrating how you can use Deci in a variety of video analytics use cases.
A defense company needed to process high resolution
images for an object detection and tracking task on an
NVIDIA Jetson Xavier NX device. In order for the system to
become operational, the customer they needed to run in a
10 watt mode and achieve a throughput of 10 frames per
second.
Using Deci’s AutoNAC engine, the customer was able to
increase throughput by 3.1X, and run smooth object
tracking, and unlock a new security application.
A security company’s goal was to maximize the efficiency of their existing infrastructure by increasing the number of live video streams that can be processed in real-time on their NVIDIA Jetson Xavier NX hardware.
However, after increasing the number of video streams to be processed, their object detection model (YOLOX) did not achieve the required level of throughput (192 frames per seconds, inference batch of 8) for the solution to be viable.
By using Deci’s AutoNAC engine, the customer built an architecture that delivered 1.7X acceleration reaching a throughput of 192 frames per seconds, while also improving the accuracy by 1% mAP. This allowed the customer to double the number of video streams from 4 to 8, increasing the scalability and profitability of their video analytics solution.
Lior Hakim, Co-Founder & CTO
Hour One
Deci is used by AI-powered video analytics solution providers to develop and optimize a wide range of applications. Here are some examples.
Smart Cities
Security
Industrial and Manufacturing
Retail and Logistics
Agriculture
Robotics
The Deci platform is used by data scientists and machine learning engineers to build, optimize, and deploy highly accurate and efficient models to production. Teams can easily develop production grade models and gain unparalleled accuracy and speed tailored for any performance targets and hardware environment. Deci is powered by AutoNAC (Automated Neural Architecture Construction), the most advanced and commercially scalable Neural Architecture Search engine in the market. AutoNAC performs a multi-constraints search to find the architecture that delivers the highest accuracy for any performance targets and hardware environment.
Build accurate & efficient architectures tailored to your hardware and application’s performance targets with Deci’s Neural Architecture Search engine.
Easily compile and quantize your models (FP16/INT8) and evaluate different production settings with a click of a button.
Train models with SuperGradients. Leverage custom recipes and advanced training techniques (e.g. knowledge distillation, quantization-aware training) with one line of code.
Deploy your models with Infery, Deci’s simple-to-use, unified, model inference API. Streamline deployment and boost serving performance with parallelism and concurrent execution. Compatible with multiple frameworks and hardware.
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")