Advanced driver-assistance systems powered by deep learning models are transforming the mobility and transportation industry. However, a common barrier to deployment is the inability to achieve high accuracy and real-time inference performance on edge devices. Both factors are absolutely mission critical for ensuring not only the application’s usability, but also safety for the users.
Watch a compilation of clips demonstrating how you can use Deci to develop a variety of automotive use cases.
Deci is used by companies in the automotive industry to develop and optimize a wide range of applications. Here are some examples.
Advanced Driver Assistance Systems
Automatic Number Plate Recognition
Occupant Monitoring System
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.
from transformers import AutoFeatureExtractor, AutoModelForImageClassification extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")