Advanced Driver Assistance Systems (ADAS) technology powered by deep learning models is transforming the mobility and transportation industry. However, AI developers are still facing an uphill battle when trying to transition from the lab to real-world deployments. 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.
Leading automotive manufacturers and ADAS providers use Deci to boost their models’ performance and deploy multiple models on edge devices.
An automotive company running a U-Net based segmentation model on a NVIDIA Jetson Xavier NX struggled to achieve the target latency in production.
Using Deci’s AutoNAC engine, a faster and smaller model was generated. Latency was reduced by 2.1x, model size was reduced by 3x, and memory footprint was reduced by 3x – all while maintaining the original accuracy.
Improve latency and throughput, and reduce model size by up to 5X while maintaining the model’s accuracy.
Maximize hardware utilization and cost-efficiently scale your solution at the edge.
Simplify development with automated tools that guarantee success.
Choose an ultra performant model or generate a custom one.
AutoNAC
Neural Architecture Search Engine
DataGradients™
Dataset Analyzer
Use Deci’s library & custom recipe to train on-prem.
SuperGradients™
PyTorch Training Library
Apply acceleration techniques. Run self-hosted inference anywhere.
Infery
Optimization & Inference Engine SDK
Tell us about your use case, needs, goals, and the obstacles in your way. We’ll show you how you can use the Deci platform to overcome them.
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Certified
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")