Navigating the delicate balance between accuracy and latency is crucial for your smart retail solutions. High accuracy is paramount, but must be paired with low latency for real-time responses. Furthermore, cost-efficient scaling of inference is essential to meet varying demand without sacrificing performance. Any imbalance can disrupt the user experience, causing delays or inaccuracies. Your challenge lies in harmonizing these elements for smooth shopper interactions.
Watch a compilation of clips demonstrating how you can use Deci in a variety of retail use cases
ResNet is an artificial neural network. It is a gateless or open-gated variant of the HighwayNet, the first working very deep feedforward neural network with hundreds of layers. In this walkthrough, Harpreet Sahota, DevRel Manager at Deci, demonstrates ResNet in action. You can check out the notebook here and follow along!
EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Join Harpreet Sahota, DevRel Manager at Deci, for a walkthrough of ResNet in action. You can check out the notebook and follow along!
RegNet is a highly flexible network design space defined by a quantized linear function. It can be specified and scaled for high efficiency or high accuracy. Join Harpreet Sahota, DevRel Manager at Deci, for a walkthrough of RegNet in action, using it to determine whether an image is Santa or not. You can check out the notebook and follow along!
Deci is used by retailers and smart retail solution providers to develop and optimize a wide range of applications. Here are some examples.
Supply Chain Management
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")